{"id":13737303,"url":"https://github.com/Guang000/Awesome-Dataset-Distillation","last_synced_at":"2025-05-08T13:33:43.572Z","repository":{"id":40630663,"uuid":"501453265","full_name":"Guang000/Awesome-Dataset-Distillation","owner":"Guang000","description":"A curated list of awesome papers on dataset distillation and related applications.","archived":false,"fork":false,"pushed_at":"2024-10-24T15:59:41.000Z","size":4330,"stargazers_count":1397,"open_issues_count":0,"forks_count":132,"subscribers_count":30,"default_branch":"main","last_synced_at":"2024-10-29T15:38:15.588Z","etag":null,"topics":["awesome-list","deep-learning"],"latest_commit_sha":null,"homepage":"https://guang000.github.io/Awesome-Dataset-Distillation/","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Guang000.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"citations/bohdal2020flexible.txt","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-06-09T00:32:32.000Z","updated_at":"2024-10-25T11:16:07.000Z","dependencies_parsed_at":"2022-07-14T04:10:41.967Z","dependency_job_id":"93761ef8-e300-4431-883f-8ff78cafe315","html_url":"https://github.com/Guang000/Awesome-Dataset-Distillation","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Guang000%2FAwesome-Dataset-Distillation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Guang000%2FAwesome-Dataset-Distillation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Guang000%2FAwesome-Dataset-Distillation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Guang000%2FAwesome-Dataset-Distillation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Guang000","download_url":"https://codeload.github.com/Guang000/Awesome-Dataset-Distillation/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223073016,"owners_count":17083076,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["awesome-list","deep-learning"],"created_at":"2024-08-03T03:01:40.454Z","updated_at":"2025-05-08T13:33:43.553Z","avatar_url":"https://github.com/Guang000.png","language":"HTML","funding_links":[],"categories":["Others","其他_机器学习与深度学习","Related Repositories","Other Lists","HTML","Papers"],"sub_categories":["Security","TeX Lists","Single-Modality: Vision (Image-only)"],"readme":"# Awesome Dataset Distillation \n\n[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n\u003cimg src=\"https://img.shields.io/badge/Contributions-Welcome-278ea5\" alt=\"Contrib\"/\u003e \u003cimg src=\"https://img.shields.io/badge/Number%20of%20Items-237-FF6F00\" alt=\"PaperNum\"/\u003e ![Stars](https://img.shields.io/github/stars/Guang000/Awesome-Dataset-Distillation?color=yellow\u0026label=Stars) ![Forks](https://img.shields.io/github/forks/Guang000/Awesome-Dataset-Distillation?color=green\u0026label=Forks)\n\n**Awesome Dataset Distillation** provides the most comprehensive and detailed information on the Dataset Distillation field.\n\n**Dataset distillation** is the task of synthesizing a small dataset such that models trained on it achieve high performance on the original large dataset. A dataset distillation algorithm takes as **input** a large real dataset to be distilled (training set), and **outputs** a small synthetic distilled dataset, which is evaluated via testing models trained on this distilled dataset on a separate real dataset (validation/test set). A good small distilled dataset is not only useful in dataset understanding, but has various applications (e.g., continual learning, privacy, neural architecture search, etc.). This task was first introduced in the paper [*Dataset Distillation* [Tongzhou Wang et al., '18]](https://www.tongzhouwang.info/dataset_distillation/), along with a proposed algorithm using backpropagation through optimization steps. Then the task was first extended to the real-world datasets in the paper [*Medical Dataset Distillation* [Guang Li et al., '19]](https://arxiv.org/abs/2104.02857), which also explored the privacy preservation possibilities of dataset distillation. In the paper [*Dataset Condensation* [Bo Zhao et al., '20]](https://arxiv.org/abs/2006.05929), gradient matching was first introduced and greatly promoted the development of the dataset distillation field.\n\nIn recent years (2022-now), dataset distillation has gained increasing attention in the research community, across many institutes and labs. More papers are now being published each year. These wonderful researches have been constantly improving dataset distillation and exploring its various variants and applications.\n\n**This project is curated and maintained by [Guang Li](https://www-lmd.ist.hokudai.ac.jp/member/guang-li/), [Bo Zhao](https://www.bozhao.me/), and [Tongzhou Wang](https://www.tongzhouwang.info/).**\n\n\u003cimg src=\"./images/logo.jpg\" width=\"20%\"/\u003e\n\n#### [How to submit a pull request?](./CONTRIBUTING.md)\n\n+ :globe_with_meridians: Project Page\n+ :octocat: Code\n+ :book: `bibtex`\n\n## Latest Updates\n+ [2025/04/25] [Permutation-Invariant and Orientation-Aware Dataset Distillation for 3D Point Clouds](https://arxiv.org/abs/2503.22154) (Jae-Young Yim \u0026 Dongwook Kim et al., 2025) [:book:](./citations/yim2025point.txt)\n+ [2025/03/26] [Curriculum Coarse-to-Fine Selection for High-IPC Dataset Distillation](https://arxiv.org/abs/2503.18872) (Yanda Chen \u0026 Gongwei Chen et al., CVPR 2025) [:octocat:](https://github.com/CYDaaa30/CCFS) [:book:](./citations/chen2025ccfs.txt)\n+ [2025/03/26] [Enhancing Dataset Distillation via Non-Critical Region Refinement](https://arxiv.org/abs/2503.18267) (Minh-Tuan Tran et al., CVPR 2025) [:octocat:](https://github.com/tmtuan1307/NRR-DD) [:book:](./citations/tran2025nrrdd.txt)\n+ [2025/03/25] [Condensing Action Segmentation Datasets via Generative Network Inversion](https://arxiv.org/abs/2503.14112) (Guodong Ding et al., CVPR 2025) [:book:](./citations/ding2025video.txt)\n+ [2025/03/10] [DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation](https://arxiv.org/abs/2411.19946) (Zhiqiang Shen \u0026 Ammar Sherif et al., CVPR 2025) [:octocat:](https://github.com/VILA-Lab/DELT) [:book:](./citations/shen2025delt.txt)\n+ [2025/03/07] [Hierarchical Features Matter: A Deep Exploration of GAN Priors for Improved Dataset Distillation](https://arxiv.org/abs/2406.05704) (Xinhao Zhong \u0026 Hao Fang et al., CVPR 2025) [:octocat:](https://github.com/ndhg1213/H-GLaD) [:book:](./citations/zhong2025hglad.txt)\n+ [2025/03/04] [Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory](https://arxiv.org/abs/2406.19827) (Wenliang Zhong et al., CVPR 2025) [:octocat:](https://github.com/Zhong0x29a/MCT) [:book:](./citations/zhong2025mct.txt)\n+ [2025/03/04] [Dataset Distillation with Neural Characteristic Function: A Minmax Perspective](https://arxiv.org/abs/2502.20653) (Shaobo Wang et al., CVPR 2025) [:octocat:](https://github.com/gszfwsb/NCFM) [:book:](./citations/wang2025ncfm.txt)\n+ [2025/02/26] [Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios](https://arxiv.org/abs/2410.17193) (Kai Wang \u0026 Zekai Li et al., CVPR 2025) [:octocat:](https://github.com/NUS-HPC-AI-Lab/EDF) [:book:](./citations/wang2025edf.txt)\n+ [2025/02/26] [Distilling Long-tailed Datasets](https://arxiv.org/abs/2408.14506) (Zhenghao Zhao \u0026 Haoxuan Wang et al., CVPR 2025) [:octocat:](https://github.com/ichbill/LTDD) [:book:](./citations/zhao2025long.txt)\n\n## Contents\n- [Main](#main)\n  - [Early Work](#early-work)\n  - [Gradient/Trajectory Matching Surrogate Objective](#gradient-objective)\n  - [Distribution/Feature Matching Surrogate Objective](#feature-objective)\n  - [Kernel-Based Distillation](#kernel)\n  - [Distilled Dataset Parametrization](#parametrization)\n  - [Generative Distillation](#generative)\n  - [Better Optimization](#optimization)\n  - [Better Understanding](#understanding)\n  - [Label Distillation](#label)\n  - [Dataset Quantization](#quant)\n  - [Decoupled Distillation](#decouple)\n  - [Multimodal Distillation](#multi)\n  - [Self-Supervised Distillation](#self)\n  - [Benchmark](#benchmark)\n  - [Survey](#survey)\n  - [Ph.D. Thesis](#thesis)\n  - [Workshop](#workshop)\n  - [Challenge](#challenge)\n  - [Ranking](#ranking)\n- [Applications](#applications)\n  - [Continual Learning](#continual)\n  - [Privacy](#privacy)\n  - [Medical](#medical)\n  - [Federated Learning](#fed)\n  - [Graph Neural Network](#gnn)\n  - [Neural Architecture Search](#nas)\n  - [Fashion, Art, and Design](#fashion)\n  - [Recommender Systems](#rec)\n  - [Blackbox Optimization](#blackbox)\n  - [Robustness](#robustness)\n  - [Fairness](#fairness)\n  - [Text](#text)\n  - [Tabular](#tabular)\n  - [Retrieval](#retrieval)\n  - [Video](#video)\n  - [Domain Adaptation](#domain)\n  - [Super Resolution](#super)\n  - [Time Series](#time)\n  - [Speech](#speech)\n  - [Machine Unlearning](#unlearning)\n  - [Reinforcement Learning](#rl)\n  - [Long-Tail](#long)\n  - [Learning with Noisy Labels](#noisy)\n  - [Object Detection](#detection)\n  - [Point Cloud](#point)\n\u003ca name=\"main\" /\u003e\n\n## Main\n+ [Dataset Distillation](https://arxiv.org/abs/1811.10959) (Tongzhou Wang et al., 2018) [:globe_with_meridians:](https://ssnl.github.io/dataset_distillation/) [:octocat:](https://github.com/SsnL/dataset-distillation) [:book:](./citations/wang2018datasetdistillation.txt)\n\n\u003ca name=\"early-work\" /\u003e\n\n### Early Work\n+ [Gradient-Based Hyperparameter Optimization Through Reversible Learning](https://arxiv.org/abs/1502.03492) (Dougal Maclaurin et al., ICML 2015) [:octocat:](https://github.com/HIPS/hypergrad) [:book:](./citations/maclaurin2015gradient.txt)\n\n\u003ca name=\"gradient-objective\" /\u003e\n\n### Gradient/Trajectory Matching Surrogate Objective\n+ [Dataset Condensation with Gradient Matching](https://arxiv.org/abs/2006.05929) (Bo Zhao et al., ICLR 2021) [:octocat:](https://github.com/VICO-UoE/DatasetCondensation) [:book:](./citations/zhao2021datasetcondensation.txt)\n+ [Dataset Condensation with Differentiable Siamese Augmentation](https://arxiv.org/abs/2102.08259) (Bo Zhao et al., ICML 2021) [:octocat:](https://github.com/VICO-UoE/DatasetCondensation) [:book:](./citations/zhao2021differentiatble.txt)\n+ [Dataset Distillation by Matching Training Trajectories](https://arxiv.org/abs/2203.11932) (George Cazenavette et al., CVPR 2022) [:globe_with_meridians:](https://georgecazenavette.github.io/mtt-distillation/) [:octocat:](https://github.com/georgecazenavette/mtt-distillation) [:book:](./citations/cazenavette2022dataset.txt)\n+ [Dataset Condensation with Contrastive Signals](https://arxiv.org/abs/2202.02916) (Saehyung Lee et al., ICML 2022) [:octocat:](https://github.com/saehyung-lee/dcc) [:book:](./citations/lee2022dataset.txt)\n+ [Loss-Curvature Matching for Dataset Selection and Condensation](https://arxiv.org/abs/2303.04449) (Seungjae Shin \u0026 Heesun Bae et al., AISTATS 2023) [:octocat:](https://github.com/SJShin-AI/LCMat) [:book:](./citations/shin2023lcmat.txt)\n+ [Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation](https://arxiv.org/abs/2211.11004) (Jiawei Du \u0026 Yidi Jiang et al., CVPR 2023) [:octocat:](https://github.com/AngusDujw/FTD-distillation) [:book:](./citations/du2023minimizing.txt)\n+ [Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory](https://arxiv.org/abs/2211.10586) (Justin Cui et al., ICML 2023) [:octocat:](https://github.com/justincui03/tesla) [:book:](./citations/cui2022scaling.txt) \n+ [Sequential Subset Matching for Dataset Distillation](https://arxiv.org/abs/2311.01570) (Jiawei Du et al., NeurIPS 2023) [:octocat:](https://github.com/shqii1j/seqmatch) [:book:](./citations/du2023seqmatch.txt)\n+ [Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching](https://arxiv.org/abs/2310.05773) (Ziyao Guo \u0026 Kai Wang et al., ICLR 2024) [:globe_with_meridians:](https://gzyaftermath.github.io/DATM/) [:octocat:](https://github.com/GzyAftermath/DATM) [:book:](./citations/guo2024datm.txt)\n+ [SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching](https://arxiv.org/abs/2406.18561) (Yongmin Lee et al., ICML 2024) [:octocat:](https://github.com/Yongalls/SelMatch) [:book:](./citations/lee2024selmatch.txt)\n+ [Dataset Distillation by Automatic Training Trajectories](https://arxiv.org/abs/2407.14245) (Dai Liu et al., ECCV 2024) [:octocat:](https://github.com/NiaLiu/ATT) [:book:](./citations/liu2024att.txt)\n+ [Neural Spectral Decomposition for Dataset Distillation](https://arxiv.org/abs/2408.16236) (Shaolei Yang et al., ECCV 2024) [:octocat:](https://github.com/slyang2021/NSD) [:book:](./citations/yang2024nsd.txt)\n+ [Prioritize Alignment in Dataset Distillation](https://arxiv.org/abs/2408.03360) (Zekai Li \u0026 Ziyao Guo et al., 2024) [:octocat:](https://github.com/NUS-HPC-AI-Lab/PAD) [:book:](./citations/li2024pad.txt)\n+ [Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory](https://arxiv.org/abs/2406.19827) (Wenliang Zhong et al., CVPR 2025) [:octocat:](https://github.com/Zhong0x29a/MCT) [:book:](./citations/zhong2025mct.txt)\n+ [Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios](https://arxiv.org/abs/2410.17193) (Kai Wang \u0026 Zekai Li et al., CVPR 2025) [:octocat:](https://github.com/NUS-HPC-AI-Lab/EDF) [:book:](./citations/wang2025edf.txt)\n\n\u003ca name=\"feature-objective\" /\u003e\n\n### Distribution/Feature Matching Surrogate Objective\n+ [CAFE: Learning to Condense Dataset by Aligning Features](https://arxiv.org/abs/2203.01531) (Kai Wang \u0026 Bo Zhao et al., CVPR 2022) [:octocat:](https://github.com/kaiwang960112/cafe) [:book:](./citations/wang2022cafe.txt)\n+ [Dataset Condensation with Distribution Matching](https://arxiv.org/abs/2110.04181) (Bo Zhao et al., WACV 2023) [:octocat:](https://github.com/VICO-UoE/DatasetCondensation) [:book:](./citations/zhao2023distribution.txt)\n+ [Improved Distribution Matching for Dataset Condensation](https://arxiv.org/abs/2307.09742) (Ganlong Zhao et al., CVPR 2023) [:octocat:](https://github.com/uitrbn/IDM) [:book:](./citations/zhao2023idm.txt)\n+ [DataDAM: Efficient Dataset Distillation with Attention Matching](https://arxiv.org/abs/2310.00093) (Ahmad Sajedi \u0026 Samir Khaki et al., ICCV 2023) [:globe_with_meridians:](https://datadistillation.github.io/DataDAM/) [:octocat:](https://github.com/DataDistillation/DataDAM) [:book:](./citations/sajedi2023datadam.txt)\n+ [Dataset Distillation via the Wasserstein Metric](https://arxiv.org/abs/2311.18531) (Haoyang Liu et al., 2023) [:book:](./citations/liu2023wasserstein.txt)\n+ [M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy](https://arxiv.org/abs/2312.15927) (Hansong Zhang \u0026 Shikun Li et al., AAAI 2024)  [:octocat:](https://github.com/Hansong-Zhang/M3D) [:book:](./citations/zhang2024m3d.txt)\n+ [Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation](https://arxiv.org/abs/2404.00563) (Wenxiao Deng et al., CVPR 2024) [:octocat:](https://github.com/VincenDen/IID) [:book:](./citations/deng2024iid.txt)\n+ [Dataset Condensation with Latent Quantile Matching](https://openaccess.thecvf.com/content/CVPR2024W/DDCV/html/Wei_Dataset_Condensation_with_Latent_Quantile_Matching_CVPRW_2024_paper.html) (Wei Wei et al., CVPR 2024 Workshop) [:book:](./citations/wei2024lqm.txt)\n+ [DANCE: Dual-View Distribution Alignment for Dataset Condensation](https://arxiv.org/abs/2406.01063) (Hansong Zhang et al., IJCAI 2024) [:octocat:](https://github.com/Hansong-Zhang/DANCE) [:book:](./citations/zhang2024dance.txt)\n+ [Diversified Semantic Distribution Matching for Dataset Distillation](https://dl.acm.org/doi/10.1145/3664647.3680900) (Hongcheng Li et al., MM 2024) [:octocat:](https://github.com/Li-Hongcheng/DSDM) [:book:](./citations/li2024dsdm.txt)\n+ [Dataset Distillation with Neural Characteristic Function: A Minmax Perspective](https://arxiv.org/abs/2502.20653) (Shaobo Wang et al., CVPR 2025) [:octocat:](https://github.com/gszfwsb/NCFM) [:book:](./citations/wang2025ncfm.txt)\n \n\u003ca name=\"kernel\" /\u003e\n\n### Kernel-Based Distillation\n+ [Dataset Meta-Learning from Kernel Ridge-Regression](https://arxiv.org/abs/2011.00050) (Timothy Nguyen et al., ICLR 2021) [:octocat:](https://github.com/google/neural-tangents) [:book:](./citations/nguyen2021kip.txt)\n+ [Dataset Distillation with Infinitely Wide Convolutional Networks](https://arxiv.org/abs/2107.13034) (Timothy Nguyen et al., NeurIPS 2021) [:octocat:](https://github.com/google/neural-tangents) [:book:](./citations/nguyen2021kipimprovedresults.txt)\n+ [Dataset Distillation using Neural Feature Regression](https://arxiv.org/abs/2206.00719) (Yongchao Zhou et al., NeurIPS 2022) [:globe_with_meridians:](https://sites.google.com/view/frepo) [:octocat:](https://github.com/yongchao97/FRePo) [:book:](./citations/zhou2022dataset.txt)\n+ [Efficient Dataset Distillation using Random Feature Approximation](https://arxiv.org/abs/2210.12067) (Noel Loo et al., NeurIPS 2022) [:octocat:](https://github.com/yolky/RFAD) [:book:](./citations/loo2022efficient.txt)\n+ [Dataset Distillation with Convexified Implicit Gradients](https://arxiv.org/abs/2302.06755) (Noel Loo et al., ICML 2023) [:octocat:](https://github.com/yolky/RCIG) [:book:](./citations/loo2023dataset.txt)\n+ [Provable and Efficient Dataset Distillation for Kernel Ridge Regression](https://openreview.net/forum?id=WI2VpcBdnd) (Yilan Chen et al., NeurIPS 2024) [:book:](./citations/chen2024krr.txt)\n\n\u003ca name=\"parametrization\" /\u003e\n\n### Distilled Dataset Parametrization\n+ [Dataset Condensation via Efficient Synthetic-Data Parameterization](https://arxiv.org/abs/2205.14959) (Jang-Hyun Kim et al., ICML 2022) [:octocat:](https://github.com/snu-mllab/efficient-dataset-condensation) [:book:](./citations/kim2022dataset.txt)\n+ [Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks](https://arxiv.org/abs/2206.02916) (Zhiwei Deng et al., NeurIPS 2022) [:octocat:](https://github.com/princetonvisualai/RememberThePast-DatasetDistillation) [:book:](./citations/deng2022remember.txt)\n+ [On Divergence Measures for Bayesian Pseudocoresets](https://arxiv.org/abs/2210.06205) (Balhae Kim et al., NeurIPS 2022) [:octocat:](https://github.com/balhaekim/bpc-divergences) [:book:](./citations/kim2022divergence.txt)\n+ [Dataset Distillation via Factorization](https://arxiv.org/abs/2210.16774) (Songhua Liu et al., NeurIPS 2022) [:octocat:](https://github.com/Huage001/DatasetFactorization) [:book:](./citations/liu2022dataset.txt)\n+ [PRANC: Pseudo RAndom Networks for Compacting Deep Models](https://arxiv.org/abs/2206.08464) (Parsa Nooralinejad et al., 2022) [:octocat:](https://github.com/UCDvision/PRANC) [:book:](./citations/nooralinejad2022pranc.txt)\n+ [Dataset Condensation with Latent Space Knowledge Factorization and Sharing](https://arxiv.org/abs/2208.10494) (Hae Beom Lee \u0026 Dong Bok Lee et al., 2022) [:book:](./citations/lee2022kfs.txt)\n+ [Slimmable Dataset Condensation](https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Slimmable_Dataset_Condensation_CVPR_2023_paper.html) (Songhua Liu et al., CVPR 2023) [:book:](./citations/liu2023slimmable.txt)\n+ [Few-Shot Dataset Distillation via Translative Pre-Training](https://openaccess.thecvf.com/content/ICCV2023/html/Liu_Few-Shot_Dataset_Distillation_via_Translative_Pre-Training_ICCV_2023_paper.html) (Songhua Liu et al., ICCV 2023) [:book:](./citations/liu2023fewshot.txt)\n+ [MGDD: A Meta Generator for Fast Dataset Distillation](https://openreview.net/forum?id=D9CMRR5Lof) (Songhua Liu et al., NeurIPS 2023) [:book:](./citations/liu2023mgdd.txt)\n+ [Sparse Parameterization for Epitomic Dataset Distillation](https://openreview.net/forum?id=ZIfhYAE2xg) (Xing Wei \u0026 Anjia Cao et al., NeurIPS 2023) [:octocat:](https://github.com/MIV-XJTU/SPEED) [:book:](./citations/wei2023sparse.txt)\n+ [Frequency Domain-based Dataset Distillation](https://arxiv.org/abs/2311.08819) (Donghyeok Shin \u0026 Seungjae Shin et al., NeurIPS 2023) [:octocat:](https://github.com/sdh0818/FreD) [:book:](./citations/shin2023fred.txt)\n+ [Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation](https://arxiv.org/abs/2310.07506) (Haizhong Zheng et al., ECCV 2024) [:book:](./citations/zheng2024hmn.txt)\n+ [FYI: Flip Your Images for Dataset Distillation](https://arxiv.org/abs/2407.08113) (Byunggwan Son et al., ECCV 2024) [:globe_with_meridians:](https://cvlab.yonsei.ac.kr/projects/FYI/) [:octocat:](https://github.com/cvlab-yonsei/FYI) [:book:](./citations/son2024fyi.txt)\n+ [Color-Oriented Redundancy Reduction in Dataset Distillation](https://arxiv.org/abs/2411.11329) (Bowen Yuan et al., NeurIPS 2024) [:octocat:](https://github.com/KeViNYuAn0314/AutoPalette) [:book:](./citations/yuan2024color.txt)\n+ [Distilling Dataset into Neural Field](https://arxiv.org/abs/2503.04835) (Donghyeok Shin et al., ICLR 2025) [:octocat:](https://github.com/aailab-kaist/DDiF) [:book:](./citations/shin2025ddif.txt)\n\n\u003ca name=\"generative\" /\u003e\n\n### Generative Distillation\n+ [Synthesizing Informative Training Samples with GAN](https://arxiv.org/abs/2204.07513) (Bo Zhao et al., NeurIPS 2022 Workshop) [:octocat:](https://github.com/vico-uoe/it-gan) [:book:](./citations/zhao2022synthesizing.txt)\n+ [Generalizing Dataset Distillation via Deep Generative Prior](https://arxiv.org/abs/2305.01649) (George Cazenavette et al., CVPR 2023) [:globe_with_meridians:](https://georgecazenavette.github.io/glad/) [:octocat:](https://github.com/georgecazenavette/glad) [:book:](./citations/cazenavette2023glad.txt)\n+ [DiM: Distilling Dataset into Generative Model](https://arxiv.org/abs/2303.04707) (Kai Wang \u0026 Jianyang Gu et al., 2023) [:octocat:](https://github.com/vimar-gu/DiM) [:book:](./citations/wang2023dim.txt)\n+ [Dataset Condensation via Generative Model](https://arxiv.org/abs/2309.07698) (Junhao Zhang et al., 2023) [:book:](./citations/zhang2023dc.txt)\n+ [Efficient Dataset Distillation via Minimax Diffusion](https://arxiv.org/abs/2311.15529) (Jianyang Gu et al., CVPR 2024) [:octocat:](https://github.com/vimar-gu/MinimaxDiffusion) [:book:](./citations/gu2024efficient.txt)\n+ [D4M: Dataset Distillation via Disentangled Diffusion Model](https://arxiv.org/abs/2407.15138) (Duo Su \u0026 Junjie Hou et al., CVPR 2024) [:globe_with_meridians:](https://junjie31.github.io/D4M/) [:octocat:](https://github.com/suduo94/D4M) [:book:](./citations/su2024d4m.txt)\n+ [Generative Dataset Distillation: Balancing Global Structure and Local Details](https://arxiv.org/abs/2404.17732) (Longzhen Li \u0026 Guang Li et al., CVPR 2024 Workshop) [:book:](./citations/li2024generative.txt)\n+ [Data-to-Model Distillation: Data-Efficient Learning Framework](https://www.ecva.net/papers/eccv_2024/papers_ECCV/html/6020_ECCV_2024_paper.php) (Ahmad Sajedi \u0026 Samir Khaki et al., ECCV 2024) [:book:](./citations/sajedi2024data.txt)\n+ [Generative Dataset Distillation Based on Diffusion Model](https://arxiv.org/abs/2408.08610) (Duo Su \u0026 Junjie Hou \u0026 Guang Li et al., ECCV 2024 Workshop) [:octocat:](https://github.com/Guang000/Generative-Dataset-Distillation-Based-on-Diffusion-Model) [:book:](./citations/su2024diffusion.txt)\n+ [Latent Dataset Distillation with Diffusion Models](https://arxiv.org/abs/2403.03881) (Brian B. Moser \u0026 Federico Raue et al., 2024) [:book:](./citations/moser2024ld3m.txt)\n+ [Influence-Guided Diffusion for Dataset Distillation](https://openreview.net/forum?id=0whx8MhysK) (Mingyang Chen et al., ICLR 2025) [:octocat:](https://github.com/mchen725/DD_IGD) [:book:](./citations/chen2025igd.txt)\n+ [Hierarchical Features Matter: A Deep Exploration of GAN Priors for Improved Dataset Distillation](https://arxiv.org/abs/2406.05704) (Xinhao Zhong \u0026 Hao Fang et al., CVPR 2025) [:octocat:](https://github.com/ndhg1213/H-GLaD) [:book:](./citations/zhong2025hglad.txt)\n\n\u003ca name=\"optimization\" /\u003e\n\n### Better Optimization\n+ [Accelerating Dataset Distillation via Model Augmentation](https://arxiv.org/abs/2212.06152) (Lei Zhang \u0026 Jie Zhang et al., CVPR 2023) [:octocat:](https://github.com/ncsu-dk-lab/Acc-DD) [:book:](./citations/zhang2023accelerating.txt)\n+ [DREAM: Efficient Dataset Distillation by Representative Matching](https://arxiv.org/abs/2302.14416) (Yanqing Liu \u0026 Jianyang Gu \u0026 Kai Wang et al., ICCV 2023) [:octocat:](https://github.com/lyq312318224/DREAM) [:book:](./citations/liu2023dream.txt)\n+ [You Only Condense Once: Two Rules for Pruning Condensed Datasets](https://arxiv.org/abs/2310.14019) (Yang He et al., NeurIPS 2023) [:octocat:](https://github.com/he-y/you-only-condense-once) [:book:](./citations/he2023yoco.txt)\n+ [MIM4DD: Mutual Information Maximization for Dataset Distillation](https://arxiv.org/abs/2312.16627) (Yuzhang Shang et al., NeurIPS 2023) [:book:](./citations/shang2023mim4dd.txt)\n+ [Can Pre-Trained Models Assist in Dataset Distillation?](https://arxiv.org/abs/2310.03295) (Yao Lu et al., 2023) [:octocat:](https://github.com/yaolu-zjut/DDInterpreter) [:book:](./citations/lu2023pre.txt)\n+ [DREAM+: Efficient Dataset Distillation by Bidirectional Representative Matching](https://arxiv.org/abs/2310.15052) (Yanqing Liu \u0026 Jianyang Gu \u0026 Kai Wang et al., 2023) [:octocat:](https://github.com/lyq312318224/DREAM) [:book:](./citations/liu2023dream+.txt)\n+ [Dataset Distillation in Latent Space](https://arxiv.org/abs/2311.15547) (Yuxuan Duan et al., 2023) [:book:](./citations/duan2023latent.txt)\n+ [Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality](https://arxiv.org/abs/2310.06982) (Xuxi Chen \u0026 Yu Yang et al., ICLR 2024) [:octocat:](https://github.com/VITA-Group/ProgressiveDD) [:book:](./citations/chen2024vodka.txt)\n+ [Embarassingly Simple Dataset Distillation](https://arxiv.org/abs/2311.07025) (Yunzhen Feng et al., ICLR 2024) [:octocat:](https://github.com/fengyzpku/Simple_Dataset_Distillation) [:book:](./citations/yunzhen2024embarassingly.txt)\n+ [Multisize Dataset Condensation](https://arxiv.org/abs/2403.06075) (Yang He et al., ICLR 2024) [:octocat:](https://github.com/he-y/Multisize-Dataset-Condensation) [:book:](./citations/he2024mdc.txt)\n+ [Large Scale Dataset Distillation with Domain Shift](https://openreview.net/forum?id=0FWPKHMCSc) (Noel Loo \u0026 Alaa Maalouf et al., ICML 2024) [:octocat:](https://github.com/yolky/d3s_distillation) [:book:](./citations/loo2024d3s.txt)\n+ [Distill Gold from Massive Ores: Bi-level Data Pruning towards Efficient Dataset Distillation](https://arxiv.org/abs/2305.18381) (Yue Xu et al., ECCV 2024) [:octocat:](https://github.com/silicx/GoldFromOres) [:book:](./citations/xu2024distill.txt)\n+ [Towards Model-Agnostic Dataset Condensation by Heterogeneous Models](https://arxiv.org/abs/2409.14538) (Jun-Yeong Moon et al., ECCV 2024) [:octocat:](https://github.com/khu-agi/hmdc) [:book:](./citations/moon2024hmdc.txt)\n+ [Teddy: Efficient Large-Scale Dataset Distillation via Taylor-Approximated Matching](https://arxiv.org/abs/2410.07579) (Ruonan Yu et al., ECCV 2024) [:book:](./citations/yu2024teddy.txt)\n+ [BACON: Bayesian Optimal Condensation Framework for Dataset Distillation](https://arxiv.org/abs/2406.01112) (Zheng Zhou et al., 2024) [:octocat:](https://github.com/zhouzhengqd/BACON) [:book:](./citations/zhou2024bacon.txt)\n+ [Going Beyond Feature Similarity: Effective Dataset Distillation based on Class-aware Conditional Mutual Information](https://arxiv.org/abs/2412.09945) (Xinhao Zhong et al., ICLR 2025) [:octocat:](https://github.com/ndhg1213/CMIDD) [:book:](./citations/zhong2025cmi.txt)\n+ [Curriculum Coarse-to-Fine Selection for High-IPC Dataset Distillation](https://arxiv.org/abs/2503.18872) (Yanda Chen \u0026 Gongwei Chen et al., CVPR 2025) [:octocat:](https://github.com/CYDaaa30/CCFS) [:book:](./citations/chen2025ccfs.txt)\n\n\u003ca name=\"understanding\" /\u003e\n\n### Better Understanding\n+ [Optimizing Millions of Hyperparameters by Implicit Differentiation](https://arxiv.org/abs/1911.02590) (Jonathan Lorraine et al., AISTATS 2020) [:octocat:](https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Optimizing_Millions_of_Hyperparameters_by_Implicit_Differentiation) [:book:](./citations/lorraine2020optimizing.txt) \n+ [On Implicit Bias in Overparameterized Bilevel Optimization](https://proceedings.mlr.press/v162/vicol22a.html) (Paul Vicol et al., ICML 2022) [:book:](./citations/vicol2022implicit.txt)\n+ [On the Size and Approximation Error of Distilled Sets](https://arxiv.org/abs/2305.14113) (Alaa Maalouf \u0026 Murad Tukan et al., NeurIPS 2023) [:book:](./citations/maalouf2023size.txt)\n+ [A Theoretical Study of Dataset Distillation](https://openreview.net/forum?id=dq5QGXGxoJ) (Zachary Izzo et al., NeurIPS 2023 Workshop) [:book:](./citations/izzo2023theo.txt)\n+ [What is Dataset Distillation Learning?](https://arxiv.org/abs/2406.04284) (William Yang et al., ICML 2024) [:octocat:](https://github.com/princetonvisualai/What-is-Dataset-Distillation-Learning) [:book:](./citations/yang2024learning.txt)\n+ [Mitigating Bias in Dataset Distillation](https://arxiv.org/abs/2406.06609) (Justin Cui et al., ICML 2024) [:book:](./citations/cui2024bias.txt)\n+ [Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning](https://arxiv.org/abs/2409.01410) (Vyacheslav Kungurtsev et al., 2024) [:book:](./citations/kungurtsev2024first.txt)\n+ [Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation](https://arxiv.org/abs/2408.12483) (Shaobo Wang et al., 2024) [:book:](./citations/wang2024samples.txt)\n+ [Dataset Distillation as Pushforward Optimal Quantization](https://arxiv.org/abs/2501.07681) (Hongye Tan et al., 2025) [:book:](./citations/tan2025optimal.txt)\n\n\u003ca name=\"label\" /\u003e\n\n### Label Distillation\n+ [Flexible Dataset Distillation: Learn Labels Instead of Images](https://arxiv.org/abs/2006.08572) (Ondrej Bohdal et al., NeurIPS 2020 Workshop) [:octocat:](https://github.com/ondrejbohdal/label-distillation) [:book:](./citations/bohdal2020flexible.txt)\n+ [Soft-Label Dataset Distillation and Text Dataset Distillation](https://arxiv.org/abs/1910.02551) (Ilia Sucholutsky et al., IJCNN 2021) [:octocat:](https://github.com/ilia10000/dataset-distillation) [:book:](./citations/sucholutsky2021soft.txt)\n+ [A Label is Worth a Thousand Images in Dataset Distillation](https://arxiv.org/abs/2406.10485) (Tian Qin et al., NeurIPS 2024) [:octocat:](https://github.com/sunnytqin/no-distillation) [:book:](./citations/qin2024label.txt)\n+ [Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?](https://arxiv.org/abs/2410.15919) (Lingao Xiao et al., NeurIPS 2024) [:octocat:](https://github.com/he-y/soft-label-pruning-for-dataset-distillation) [:book:](./citations/xiao2024soft.txt)\n+ [DRUPI: Dataset Reduction Using Privileged Information](https://arxiv.org/abs/2410.01611) (Shaobo Wang et al., 2024) [:book:](./citations/wang2024drupi.txt)\n+ [Label-Augmented Dataset Distillation](https://arxiv.org/abs/2409.16239) (Seoungyoon Kang \u0026 Youngsun Lim et al., WACV 2025) [:book:](./citations/kang2024label.txt)\n+ [GIFT: Unlocking Full Potential of Labels in Distilled Dataset at Near-zero Cost](https://arxiv.org/abs/2405.14736) (Xinyi Shang \u0026 Peng Sun et al., ICLR 2025) [:octocat:](https://github.com/LINs-lab/GIFT) [:book:](./citations/shang2025gift.txt)\n\n\u003ca name=\"quant\" /\u003e\n\n### Dataset Quantization\n+ [Dataset Quantization](https://arxiv.org/abs/2308.10524) (Daquan Zhou \u0026 Kai Wang \u0026 Jianyang Gu et al., ICCV 2023) [:octocat:](https://github.com/magic-research/Dataset_Quantization) [:book:](./citations/zhou2023dataset.txt)\n+ [Dataset Quantization with Active Learning based Adaptive Sampling](https://arxiv.org/abs/2407.07268) (Zhenghao Zhao et al., ECCV 2024) [:octocat:](https://github.com/ichbill/DQAS) [:book:](./citations/zhao2024dqas.txt)\n+ [Adaptive Dataset Quantization](https://www.arxiv.org/abs/2412.16895) (Muquan Li et al., AAAI 2025) [:octocat:](https://github.com/SLGSP/ADQ) [:book:](./citations/li2025adq.txt)\n\n\u003ca name=\"decouple\" /\u003e\n\n### Decoupled Distillation\n+ [Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective](https://arxiv.org/abs/2306.13092) (Zeyuan Yin \u0026 Zhiqiang Shen et al., NeurIPS 2023) [:globe_with_meridians:](https://zeyuanyin.github.io/projects/SRe2L/) [:octocat:](https://github.com/VILA-Lab/SRe2L/tree/main/SRe2L) [:book:](./citations/yin2023sre2l.txt)\n+ [Dataset Distillation via Curriculum Data Synthesis in Large Data Era](https://arxiv.org/abs/2311.18838) (Zeyuan Yin et al., TMLR 2024) [:octocat:](https://github.com/VILA-Lab/SRe2L/tree/main/CDA) [:book:](./citations/yin2024cda.txt)\n+ [Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching](https://arxiv.org/abs/2311.17950) (Shitong Shao et al., CVPR 2024) [:octocat:](https://github.com/shaoshitong/G_VBSM_Dataset_Condensation) [:book:](./citations/shao2024gvbsm.txt)\n+ [On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm](https://arxiv.org/abs/2312.03526) (Peng Sun et al., CVPR 2024) [:octocat:](https://github.com/LINs-lab/RDED) [:book:](./citations/sun2024rded.txt)\n+ [Information Compensation: A Fix for Any-scale Dataset Distillation](https://openreview.net/forum?id=2SnmKd1JK4) (Peng Sun et al., ICLR 2024 Workshop) [:book:](./citations/sun2024lic.txt)\n+ [Elucidating the Design Space of Dataset Condensation](https://arxiv.org/abs/2404.13733) (Shitong Shao et al., NeurIPS 2024) [:octocat:](https://github.com/shaoshitong/EDC) [:book:](./citations/shao2024edc.txt)\n+ [Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment](https://arxiv.org/abs/2409.17612) (Jiawei Du et al., NeurIPS 2024) [:octocat:](https://github.com/AngusDujw/Diversity-Driven-Synthesis) [:book:](./citations/du2024diversity.txt)\n+ [Curriculum Dataset Distillation](https://arxiv.org/abs/2405.09150) (Zhiheng Ma \u0026 Anjia Cao et al., 2024) [:book:](./citations/ma2024cudd.txt)\n+ [Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator](https://arxiv.org/abs/2408.06927) (Xin Zhang et al., ICLR 2025) [:octocat:](https://github.com/zhangxin-xd/UFC) [:book:](./citations/zhang2025infer.txt)\n+ [DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation](https://arxiv.org/abs/2411.19946) (Zhiqiang Shen \u0026 Ammar Sherif et al., CVPR 2025) [:octocat:](https://github.com/VILA-Lab/DELT) [:book:](./citations/shen2025delt.txt)\n+ [Enhancing Dataset Distillation via Non-Critical Region Refinement](https://arxiv.org/abs/2503.18267) (Minh-Tuan Tran et al., CVPR 2025) [:octocat:](https://github.com/tmtuan1307/NRR-DD) [:book:](./citations/tran2025nrrdd.txt)\n+ [FocusDD: Real-World Scene Infusion for Robust Dataset Distillation](https://arxiv.org/abs/2501.06405) (Youbin Hu et al., 2025) [:book:](./citations/hu2025focusdd.txt)\n+ [Dataset Distillation via Committee Voting](https://arxiv.org/abs/2501.07575) (Jiacheng Cui et al., 2025) [:octocat:](https://github.com/Jiacheng8/CV-DD) [:book:](./citations/cui2025cvdd.txt)\n\n\u003ca name=\"multi\" /\u003e\n\n### Multimodal Distillation\n+ [Vision-Language Dataset Distillation](https://arxiv.org/abs/2308.07545) (Xindi Wu et al., TMLR 2024) [:globe_with_meridians:](https://princetonvisualai.github.io/multimodal_dataset_distillation/) [:octocat:](https://github.com/princetonvisualai/multimodal_dataset_distillation) [:book:](./citations/wu2024multi.txt)\n+ [Low-Rank Similarity Mining for Multimodal Dataset Distillation](https://arxiv.org/abs/2406.03793) (Yue Xu et al., ICML 2024) [:octocat:](https://github.com/silicx/LoRS_Distill) [:book:](./citations/xu2024lors.txt)\n+ [Audio-Visual Dataset Distillation](https://openreview.net/forum?id=IJlbuSrXmk) (Saksham Singh Kushwaha et al., TMLR 2024) [:octocat:](https://github.com/sakshamsingh1/AVDD) [:book:](./citations/kush2024avdd.txt)\n\n\u003ca name=\"self\" /\u003e\n\n### Self-Supervised Distillation\n+ [Self-Supervised Dataset Distillation for Transfer Learning](https://arxiv.org/abs/2310.06511) (Dong Bok Lee \u0026 Seanie Lee et al., ICLR 2024) [:octocat:](https://github.com/db-Lee/selfsup_dd) [:book:](./citations/lee2024self.txt)\n+ [Efficiency for Free: Ideal Data Are Transportable Representations](https://arxiv.org/abs/2405.14669) (Peng Sun et al., NeurIPS 2024) [:octocat:](https://github.com/LINs-lab/ReLA) [:book:](./citations/sun2024rela.txt)\n+ [Self-supervised Dataset Distillation: A Good Compression Is All You Need](https://arxiv.org/abs/2404.07976) (Muxin Zhou et al., 2024) [:octocat:](https://github.com/VILA-Lab/SRe2L/tree/main/SCDD/) [:book:](./citations/zhou2024self.txt)\n+ [Dataset Distillation via Knowledge Distillation: Towards Efficient Self-Supervised Pre-Training of Deep Networks](https://arxiv.org/abs/2410.02116) (Siddharth Joshi et al., ICLR 2025) [:octocat:](https://github.com/jiayini1119/MKDT) [:book:](./citations/joshi2025kd.txt)\n+ [Boost Self-Supervised Dataset Distillation via Parameterization, Predefined Augmentation, and Approximation](https://openreview.net/forum?id=2RfWRKwxYh) (Sheng-Feng Yu et al., ICLR 2025) [:book:](./citations/yu2025self.txt)\n\n\u003ca name=\"benchmark\" /\u003e\n\n### Benchmark\n\n+ [DC-BENCH: Dataset Condensation Benchmark](https://arxiv.org/abs/2207.09639) (Justin Cui et al., NeurIPS 2022) [:globe_with_meridians:](https://dc-bench.github.io/) [:octocat:](https://github.com/justincui03/dc_benchmark) [:book:](./citations/cui2022dc.txt)\n+ [A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness](https://arxiv.org/abs/2305.03355) (Zongxiong Chen \u0026 Jiahui Geng et al., 2023) [:book:](./citations/chen2023study.txt)\n+ [BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation](https://arxiv.org/abs/2411.09265) (Zheng Zhou et al., 2024) [:globe_with_meridians:](https://beard-leaderboard.github.io/) [:octocat:](https://github.com/zhouzhengqd/BEARD/) [:book:](./citations/zhou2024beard.txt)\n+ [DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation](https://arxiv.org/abs/2403.13322) (Yifan Wu et al., TIP 2025) [:octocat:](https://github.com/FredWU-HUST/DD-RobustBench) [:book:](./citations/wu2025robust.txt)\n\n\n\u003ca name=\"survey\" /\u003e\n\n### Survey\n\n+ [Data Distillation: A Survey](https://arxiv.org/abs/2301.04272) (Noveen Sachdeva et al., TMLR 2023) [:book:](./citations/sachdeva2023survey.txt)\n+ [A Survey on Dataset Distillation: Approaches, Applications and Future Directions](https://arxiv.org/abs/2305.01975) (Jiahui Geng \u0026 Zongxiong Chen et al., IJCAI 2023) [:octocat:](https://github.com/Guang000/Awesome-Dataset-Distillation) [:book:](./citations/geng2023survey.txt)\n+ [A Comprehensive Survey to Dataset Distillation](https://arxiv.org/abs/2301.05603) (Shiye Lei et al., TPAMI 2023) [:octocat:](https://github.com/Guang000/Awesome-Dataset-Distillation) [:book:](./citations/lei2023survey.txt)\n+ [Dataset Distillation: A Comprehensive Review](https://arxiv.org/abs/2301.07014) (Ruonan Yu \u0026 Songhua Liu et al., TPAMI 2023) [:octocat:](https://github.com/Guang000/Awesome-Dataset-Distillation) [:book:](./citations/yu2023review.txt)\n+ [The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions](https://arxiv.org/abs/2502.05673) (Ping Liu et al., 2025) [:book:](./citations/liu2025survey.txt)\n\n\u003ca name=\"thesis\" /\u003e\n\n### Ph.D. Thesis\n+ [Data-efficient Neural Network Training with Dataset Condensation](https://era.ed.ac.uk/handle/1842/39756) (Bo Zhao, The University of Edinburgh 2023) [:book:](./citations/zhao2023thesis.txt)\n\n\u003ca name=\"workshop\" /\u003e\n\n### Workshop\n+ 1st CVPR Workshop on Dataset Distillation (Saeed Vahidian et al., CVPR 2024) [:globe_with_meridians:](https://sites.google.com/view/dd-cvpr2024/home)\n\n\u003ca name=\"challenge\" /\u003e\n\n### Challenge\n+ The First Dataset Distillation Challenge (Kai Wang \u0026 Ahmad Sajedi et al., ECCV 2024) [:globe_with_meridians:](https://www.dd-challenge.com/) [:octocat:](https://github.com/DataDistillation/ECCV2024-Dataset-Distillation-Challenge)\n\n\u003ca name=\"ranking\" /\u003e\n\n### Ranking\n+ DD-Ranking: Rethinking the Evaluation of Dataset Distillation (Zekai Li \u0026 Xinhao Zhong et al., 2025) [:globe_with_meridians:](https://nus-hpc-ai-lab.github.io/DD-Ranking/) [:octocat:](https://github.com/NUS-HPC-AI-Lab/DD-Ranking) [:book:](./citations/li2025ranking.txt)\n\n## Applications\n\n\u003ca name=\"continual\" /\u003e\n\n### Continual Learning\n+ [Reducing Catastrophic Forgetting with Learning on Synthetic Data](https://arxiv.org/abs/2004.14046) (Wojciech Masarczyk et al., CVPR 2020 Workshop) [:book:](./citations/masarczyk2020reducing.txt)\n+ [Condensed Composite Memory Continual Learning](https://arxiv.org/abs/2102.09890) (Felix Wiewel et al., IJCNN 2021) [:octocat:](https://github.com/FelixWiewel/CCMCL) [:book:](./citations/wiewel2021soft.txt)\n+ [Distilled Replay: Overcoming Forgetting through Synthetic Samples](https://arxiv.org/abs/2103.15851) (Andrea Rosasco et al., IJCAI 2021 Workshop) [:octocat:](https://github.com/andrearosasco/DistilledReplay) [:book:](./citations/rosasco2021distilled.txt)\n+ [Sample Condensation in Online Continual Learning](https://arxiv.org/abs/2206.11849) (Mattia Sangermano et al., IJCNN 2022) [:octocat:](https://github.com/MattiaSangermano/OLCGM) [:book:](./citations/sangermano2022sample.txt)\n+ [An Efficient Dataset Condensation Plugin and Its Application to Continual Learning](https://openreview.net/forum?id=Murj6wcjRw) (Enneng Yang et al., NeurIPS 2023) [:octocat:](https://github.com/EnnengYang/An-Efficient-Dataset-Condensation-Plugin) [:book:](./citations/yang2023efficient.txt)\n+ [Summarizing Stream Data for Memory-Restricted Online Continual Learning](https://arxiv.org/abs/2305.16645) (Jianyang Gu et al., AAAI 2024) [:octocat:](https://github.com/vimar-gu/SSD) [:book:](./citations/gu2024ssd.txt)\n\n\u003ca name=\"privacy\" /\u003e\n\n### Privacy\n+ [Privacy for Free: How does Dataset Condensation Help Privacy?](https://arxiv.org/abs/2206.00240) (Tian Dong et al., ICML 2022) [:book:](./citations/dong2022privacy.txt)\n+ [Private Set Generation with Discriminative Information](https://arxiv.org/abs/2211.04446) (Dingfan Chen et al., NeurIPS 2022) [:octocat:](https://github.com/DingfanChen/Private-Set) [:book:](./citations/chen2022privacy.txt)\n+ [No Free Lunch in \"Privacy for Free: How does Dataset Condensation Help Privacy\"](https://arxiv.org/abs/2209.14987) (Nicholas Carlini et al., 2022) [:book:](./citations/carlini2022no.txt)\n+ [Backdoor Attacks Against Dataset Distillation](https://arxiv.org/abs/2301.01197) (Yugeng Liu et al., NDSS 2023) [:octocat:](https://github.com/liuyugeng/baadd) [:book:](./citations/liu2023backdoor.txt)\n+ [Differentially Private Kernel Inducing Points (DP-KIP) for Privacy-preserving Data Distillation](https://arxiv.org/abs/2301.13389) (Margarita Vinaroz et al., 2023) [:octocat:](https://github.com/dpclip/dpclip) [:book:](./citations/vinaroz2023dpkip.txt)\n+ [Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation](https://arxiv.org/abs/2302.01428) (Noel Loo et al., ICLR 2024) [:book:](./citations/loo2024attack.txt)\n+ [Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective](https://arxiv.org/abs/2311.16646) (Ming-Yu Chung et al., ICLR 2024) [:book:](./citations/chung2024backdoor.txt)\n+ [Differentially Private Dataset Condensation](https://www.ndss-symposium.org/ndss-paper/auto-draft-542/) (Zheng et al., NDSS 2024 Workshop) [:book:](./citations/zheng2024differentially.txt)\n+ [Adaptive Backdoor Attacks Against Dataset Distillation for Federated Learning](https://ieeexplore.ieee.org/abstract/document/10622462?casa_token=tHyZ-Pz7DpUAAAAA:vmCYI4cUcKzMluUsASHhIhr0CvBkjzBR-0N7REVj7aFN5hT5TinQTpSEsE0Bo3Fl8auh52Fipm_v) (Ze Chai et al., ICC 2024) [:book:](./citations/chai2024backdoor.txt)\n\n\u003ca name=\"medical\" /\u003e\n\n### Medical\n+ [Soft-Label Anonymous Gastric X-ray Image Distillation](https://arxiv.org/abs/2104.02857) (Guang Li et al., ICIP 2020) [:octocat:](https://github.com/Guang000/dataset-distillation) [:book:](./citations/li2020soft.txt) \n+ [Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing](https://arxiv.org/abs/2209.14635) (Guang Li et al., CMPB 2022) [:octocat:](https://github.com/Guang000/dataset-distillation) [:book:](./citations/li2022compressed.txt)\n+ [Dataset Distillation for Medical Dataset Sharing](https://r2hcai.github.io/AAAI-23/pages/accepted-papers.html) (Guang Li et al., AAAI 2023 Workshop) [:octocat:](https://github.com/Guang000/mtt-distillation) [:book:](./citations/li2023sharing.txt)\n+ [Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation](https://link.springer.com/chapter/10.1007/978-3-031-47401-9_2) (Yuchen Tian \u0026 Jiacheng Wang et al., MICCAI 2023 Workshop) [:book:](./citations/tian2023gdd.txt)\n+ [Importance-Aware Adaptive Dataset Distillation](https://arxiv.org/abs/2401.15863) (Guang Li et al., NN 2024) [:book:](./citations/li2024iadd.txt)\n+ [Image Distillation for Safe Data Sharing in Histopathology](https://arxiv.org/abs/2406.13536) (Zhe Li et al., MICCAI 2024) [:octocat:](https://github.com/ZheLi2020/InfoDist) [:book:](./citations/li2024infodist.txt)\n+ [MedSynth: Leveraging Generative Model for Healthcare Data Sharing](https://link.springer.com/chapter/10.1007/978-3-031-72390-2_61) (Renuga Kanagavelu et al., MICCAI 2024) [:book:](./citations/kanagavelu2024medsynth.txt)\n+ [Progressive Trajectory Matching for Medical Dataset Distillation](https://arxiv.org/abs/2403.13469) (Zhen Yu et al., 2024) [:book:](./citations/yu2024progressive.txt)\n+ [Dataset Distillation in Medical Imaging: A Feasibility Study](https://arxiv.org/abs/2407.14429) (Muyang Li et al., 2024) [:book:](./citations/li2024medical.txt)\n+ [Dataset Distillation for Histopathology Image Classification](https://arxiv.org/abs/2408.09709) (Cong Cong et al., 2024) [:book:](./citations/cong2024dataset.txt)\n\n\u003ca name=\"fed\" /\u003e\n\n### Federated Learning\n+ [Federated Learning via Synthetic Data](https://arxiv.org/abs/2008.04489) (Jack Goetz et al., 2020) [:book:](./citations/goetz2020federated.txt)\n+ [Distilled One-Shot Federated Learning](https://arxiv.org/abs/2009.07999) (Yanlin Zhou et al., 2020) [:book:](./citations/zhou2020distilled.txt)\n+ [DENSE: Data-Free One-Shot Federated Learning](https://arxiv.org/abs/2112.12371) (Jie Zhang \u0026 Chen Chen et al., NeurIPS 2022) [:octocat:](https://github.com/zj-jayzhang/DENSE) [:book:](./citations/zhang2022dense.txt)\n+ [FedSynth: Gradient Compression via Synthetic Data in Federated Learning](https://arxiv.org/abs/2204.01273) (Shengyuan Hu et al., 2022) [:book:](./citations/hu2022fedsynth.txt)\n+ [Meta Knowledge Condensation for Federated Learning](https://arxiv.org/abs/2209.14851) (Ping Liu et al., ICLR 2023) [:book:](./citations/liu2023meta.txt)\n+ [DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics](https://arxiv.org/abs/2211.10878) (Renjie Pi et al., CVPR 2023) [:octocat:](https://github.com/pipilurj/dynafed) [:book:](./citations/pi2023dynafed.txt)\n+ [FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning](https://arxiv.org/abs/2207.09653) (Yuanhao Xiong \u0026 Ruochen Wang et al., CVPR 2023) [:octocat:](https://github.com/anonymifish/fed-distribution-matching) [:book:](./citations/xiong2023feddm.txt)\n+ [Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments](https://arxiv.org/abs/2208.11311) (Rui Song et al., IJCNN 2023) [:octocat:](https://github.com/rruisong/fedd3) [:book:](./citations/song2023federated.txt)\n+ [FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations](https://arxiv.org/abs/2302.01068) (Hui-Po Wang et al., 2023) [:octocat:](https://github.com/a514514772/fedlap-dp) [:book:](./citations/wang2023fed.txt)\n+ [Federated Virtual Learning on Heterogeneous Data with Local-global Distillation](https://arxiv.org/abs/2303.02278) (Chun-Yin Huang et al., 2023) [:book:](./citations/huang2023federated.txt)\n+ [An Aggregation-Free Federated Learning for Tackling Data Heterogeneity](https://arxiv.org/abs/2404.18962) (Yuan Wang et al., CVPR 2024) [:book:](./citations/wang2024fed.txt)\n+ [Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors](https://arxiv.org/abs/2405.11525) (Chun-Yin Huang et al., ICML 2024) [:octocat:](https://github.com/ubc-tea/DESA) [:book:](./citations/huang2024desa.txt)\n+ [DCFL: Non-IID Awareness Dataset Condensation Aided Federated Learning](https://ieeexplore.ieee.org/document/10650791) (Xingwang Wang et al., IJCNN 2024) [:book:](./citations/wang2024dcfl.txt)\n+ [Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents](https://arxiv.org/abs/2312.01537) (Yuqi Jia \u0026 Saeed Vahidian et al., ECCV 2024) [:octocat:](https://github.com/FedDG23/FedDG-main) [:book:](./citations/jia2024feddg.txt)\n+ [One-Shot Collaborative Data Distillation](https://arxiv.org/abs/2408.02266) (William Holland et al., ECAI 2024) [:octocat:](https://github.com/rayneholland/CollabDM) [:book:](./citations/holland2024one.txt)\n+ [FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis](https://arxiv.org/abs/2412.18557) (Guochen Yan et al., AAAI 2025) [:octocat:](https://github.com/Youth-49/FedVCK_2024) [:book:](./citations/yan2025fedvck.txt)\n\n\n\u003ca name=\"gnn\" /\u003e\n\n### Graph Neural Network\n+ [Graph Condensation for Graph Neural Networks](https://arxiv.org/abs/2110.07580) (Wei Jin et al., ICLR 2022) [:octocat:](https://github.com/chandlerbang/gcond) [:book:](./citations/jin2022graph.txt)\n+ [Condensing Graphs via One-Step Gradient Matching](https://arxiv.org/abs/2206.07746) (Wei Jin et al., KDD 2022) [:octocat:](https://github.com/amazon-research/DosCond) [:book:](./citations/jin2022condensing.txt)\n+ [Graph Condensation via Receptive Field Distribution Matching](https://arxiv.org/abs/2206.13697) (Mengyang Liu et al., 2022) [:book:](./citations/liu2022graph.txt)\n+ [Kernel Ridge Regression-Based Graph Dataset Distillation](https://dl.acm.org/doi/10.1145/3580305.3599398) (Zhe Xu et al., KDD 2023) [:octocat:](https://github.com/pricexu/KIDD) [:book:](./citations/xu2023kidd.txt)\n+ [Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data](https://arxiv.org/abs/2306.02664) (Xin Zheng et al., NeurIPS 2023) [:octocat:](https://github.com/amanda-zheng/sfgc) [:book:](./citations/zheng2023sfgc.txt)\n+ [Does Graph Distillation See Like Vision Dataset Counterpart?](https://arxiv.org/abs/2310.09192) (Beining Yang \u0026 Kai Wang et al., NeurIPS 2023) [:octocat:](https://github.com/RingBDStack/SGDD) [:book:](./citations/yang2023sgdd.txt)\n+ [CaT: Balanced Continual Graph Learning with Graph Condensation](https://arxiv.org/abs/2309.09455) (Yilun Liu et al., ICDM 2023) [:octocat:](https://github.com/superallen13/CaT-CGL) [:book:](./citations/liu2023cat.txt)\n+ [Mirage: Model-Agnostic Graph Distillation for Graph Classification](https://arxiv.org/abs/2310.09486) (Mridul Gupta \u0026 Sahil Manchanda et al., ICLR 2024) [:octocat:](https://github.com/frigategnn/Mirage) [:book:](./citations/gupta2024mirage.txt)\n+ [Graph Distillation with Eigenbasis Matching](https://arxiv.org/abs/2310.09202) (Yang Liu \u0026 Deyu Bo et al., ICML 2024) [:octocat:](https://github.com/liuyang-tian/GDEM) [:book:](./citations/liu2024gdem.txt)\n+ [Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching](https://arxiv.org/abs/2402.05011) (Yuchen Zhang \u0026 Tianle Zhang \u0026 Kai Wang et al., ICML 2024) [:octocat:](https://github.com/nus-hpc-ai-lab/geom) [:book:](./citations/zhang2024geom.txt)\n+ [Graph Data Condensation via Self-expressive Graph Structure Reconstruction](https://arxiv.org/abs/2403.07294) (Zhanyu Liu \u0026 Chaolv Zeng et al., KDD 2024) [:octocat:](https://github.com/zclzcl0223/GCSR) [:book:](./citations/liu2024gcsr.txt)\n+ [Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching](https://arxiv.org/abs/2402.04924) (Tianle Zhang \u0026 Yuchen Zhang \u0026 Kai Wang et al., 2024) [:octocat:](https://github.com/nus-hpc-ai-lab/ctrl) [:book:](./citations/zhang2024ctrl.txt)\n\n\n#### Survey\n+ [A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation](https://arxiv.org/abs/2402.03358) (Mohammad Hashemi et al., IJCAI 2024) [:octocat:](https://github.com/Emory-Melody/awesome-graph-reduction) [:book:](./citations/hashemi2024awesome.txt)\n+ [A Survey on Graph Condensation](https://arxiv.org/abs/2402.02000) (Hongjia Xu et al., 2024) [:octocat:](https://github.com/Frostland12138/Awesome-Graph-Condensation) [:book:](./citations/xu2024survey.txt)\n+ [Graph Condensation: A Survey](https://arxiv.org/abs/2401.11720) (Xinyi Gao et al., TKDE 2025) [:octocat:](https://github.com/xygaog/graph-condensation-papers) [:book:](./citations/gao2025graph.txt)\n\n#### Benchmark\n+ [GC-Bench: An Open and Unified Benchmark for Graph Condensation](https://arxiv.org/abs/2407.00615) (Qingyun Sun \u0026 Ziying Chen et al., NeurIPS 2024) [:octocat:](https://github.com/RingBDStack/GC-Bench) [:book:](./citations/sun2024gcbench.txt)\n+ [GCondenser: Benchmarking Graph Condensation](https://arxiv.org/abs/2405.14246) (Yilun Liu et al., 2024) [:octocat:](https://github.com/superallen13/GCondenser) [:book:](./citations/liu2024gcondenser.txt)\n+ [GC-Bench: A Benchmark Framework for Graph Condensation with New Insights](https://arxiv.org/abs/2406.16715) (Shengbo Gong \u0026 Juntong Ni et al., 2024) [:octocat:](https://github.com/Emory-Melody/GraphSlim) [:book:](./citations/gong2024graphslim.txt)\n\n#### No further updates will be made regarding graph distillation topics as sufficient papers and summary projects are already available on the subject\n\n\u003ca name=\"nas\" /\u003e\n\n### Neural Architecture Search\n+ [Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data](https://arxiv.org/abs/1912.07768) (Felipe Petroski Such et al., ICML 2020) [:octocat:](https://github.com/uber-research/GTN) [:book:](./citations/such2020generative.txt)\n+ [Learning to Generate Synthetic Training Data using Gradient Matching and Implicit Differentiation](https://arxiv.org/abs/2203.08559) (Dmitry Medvedev et al., AIST 2021) [:octocat:](https://github.com/dm-medvedev/efficientdistillation) [:book:](./citations/medvedev2021tabular.txt)\n+ [Calibrated Dataset Condensation for Faster Hyperparameter Search](https://arxiv.org/abs/2405.17535) (Mucong Ding et al., 2024) [:book:](./citations/ding2024hcdc.txt)\n\n\u003ca name=\"fashion\" /\u003e\n\n### Fashion, Art, and Design\n+ [Wearable ImageNet: Synthesizing Tileable Textures via Dataset Distillation](https://openaccess.thecvf.com/content/CVPR2022W/CVFAD/html/Cazenavette_Wearable_ImageNet_Synthesizing_Tileable_Textures_via_Dataset_Distillation_CVPRW_2022_paper.html) (George Cazenavette et al., CVPR 2022 Workshop) [:globe_with_meridians:](https://georgecazenavette.github.io/mtt-distillation/) [:octocat:](https://github.com/georgecazenavette/mtt-distillation) [:book:](./citations/cazenavette2022textures.txt)\n+ [Learning from Designers: Fashion Compatibility Analysis Via Dataset Distillation](https://ieeexplore.ieee.org/document/9897234) (Yulan Chen et al., ICIP 2022) [:book:](./citations/chen2022fashion.txt)\n+ [Galaxy Dataset Distillation with Self-Adaptive Trajectory Matching](https://arxiv.org/abs/2311.17967) (Haowen Guan et al., NeurIPS 2023 Workshop) [:octocat:](https://github.com/HaowenGuan/Galaxy-Dataset-Distillation) [:book:](./citations/guan2023galaxy.txt)\n\n\u003ca name=\"rec\" /\u003e\n\n### Recommender Systems\n+ [Infinite Recommendation Networks: A Data-Centric Approach](https://arxiv.org/abs/2206.02626) (Noveen Sachdeva et al., NeurIPS 2022) [:octocat:](https://github.com/noveens/distill_cf) [:book:](./citations/sachdeva2022data.txt)\n+ [Gradient Matching for Categorical Data Distillation in CTR Prediction](https://dl.acm.org/doi/10.1145/3604915.3608769) (Chen Wang et al., RecSys 2023) [:book:](./citations/wang2023cgm.txt)\n+ [TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation](https://arxiv.org/abs/2502.02854) (Jiaqing Zhang et al., WWW 2025) [:octocat:](https://github.com/USTC-StarTeam/TD3) [:book:](./citations/zhang2025td3.txt)\n\n\u003ca name=\"blackbox\" /\u003e\n\n### Blackbox Optimization\n+ [Bidirectional Learning for Offline Infinite-width Model-based Optimization](https://arxiv.org/abs/2209.07507) (Can Chen et al., NeurIPS 2022) [:octocat:](https://github.com/ggchen1997/bdi) [:book:](./citations/chen2022bidirectional.txt) \n+ [Bidirectional Learning for Offline Model-based Biological Sequence Design](https://arxiv.org/abs/2301.02931) (Can Chen et al., ICML 2023) [:octocat:](https://github.com/GGchen1997/BIB-ICML2023-Submission) [:book:](./citations/chen2023bidirectional.txt)\n\n\u003ca name=\"robustness\" /\u003e\n\n### Robustness\n+ [Can We Achieve Robustness from Data Alone?](https://arxiv.org/abs/2207.11727) (Nikolaos Tsilivis et al., ICML 2022 Workshop) [:book:](./citations/tsilivis2022robust.txt)\n+ [Towards Robust Dataset Learning](https://arxiv.org/abs/2211.10752) (Yihan Wu et al., 2022) [:book:](./citations/wu2022towards.txt)\n+ [Rethinking Data Distillation: Do Not Overlook Calibration](https://arxiv.org/abs/2307.12463) (Dongyao Zhu et al., ICCV 2023) [:book:](./citations/zhu2023calibration.txt)\n+ [Towards Trustworthy Dataset Distillation](https://arxiv.org/abs/2307.09165) (Shijie Ma et al., PR 2024) [:octocat:](https://github.com/mashijie1028/TrustDD/)  [:book:](./citations/ma2024trustworthy.txt)\n+ [Towards Adversarially Robust Dataset Distillation by Curvature Regularization](https://arxiv.org/abs/2403.10045) (Eric Xue et al., AAAI 2025) [:book:](./citations/xue2025robust.txt)\n+ [Group Distributionally Robust Dataset Distillation with Risk Minimization](https://arxiv.org/abs/2402.04676) (Saeed Vahidian \u0026 Mingyu Wang \u0026 Jianyang Gu et al., ICLR 2025) [:octocat:](https://github.com/Mming11/RobustDatasetDistillation) [:book:](./citations/vahidian2025group.txt)\n\n\u003ca name=\"fairness\" /\u003e\n\n### Fairness\n+ [Fair Graph Distillation](https://openreview.net/forum?id=xW0ayZxPWs) (Qizhang Feng et al., NeurIPS 2023) [:book:](./citations/feng2023fair.txt)\n+ [FairDD: Fair Dataset Distillation via Synchronized Matching](https://arxiv.org/abs/2411.19623) (Qihang Zhou et al., 2024) [:book:](./citations/zhou2024fair.txt)\n\n\n\u003ca name=\"text\" /\u003e\n\n### Text\n+ [Data Distillation for Text Classification](https://arxiv.org/abs/2104.08448) (Yongqi Li et al., 2021) [:book:](./citations/li2021text.txt)\n+ [Dataset Distillation with Attention Labels for Fine-tuning BERT](https://aclanthology.org/2023.acl-short.12/) (Aru Maekawa et al., ACL 2023) [:octocat:](https://github.com/arumaekawa/dataset-distillation-with-attention-labels) [:book:](./citations/maekawa2023text.txt)\n+ [DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation](https://arxiv.org/abs/2404.00264) (Aru Maekawa et al., NAACL 2024) [:octocat:](https://github.com/arumaekawa/DiLM) [:book:](./citations/maekawa2024dilm.txt)\n+ [Textual Dataset Distillation via Language Model Embedding](https://aclanthology.org/2024.findings-emnlp.733/) (Yefan Tao et al., EMNLP 2024) [:book:](./citations/tao2024textual.txt)\n+ [UniDetox: Universal Detoxification of Large Language Models via Dataset Distillation](https://arxiv.org/abs/2504.20500) (Huimin Lu et al., ICLR 2025) [:octocat:](https://github.com/EminLU/UniDetox) [:book:](./citations/lu2025llm.txt)\n+ [Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training](https://arxiv.org/abs/2501.15108) (Xunxin Cai \u0026 Chengrui Wang \u0026 Qingqing Long et al., DASFAA 2025) [:book:](./citations/cai2025llm.txt)\n\n\u003ca name=\"tabular\" /\u003e\n\n### Tabular\n+ [New Properties of the Data Distillation Method When Working With Tabular Data](https://arxiv.org/abs/2010.09839) (Dmitry Medvedev et al., AIST 2020) [:octocat:](https://github.com/dm-medvedev/dataset-distillation) [:book:](./citations/medvedev2020tabular.txt)\n\n\u003ca name=\"retrieval\" /\u003e\n\n### Retrieval\n+ [Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching](https://arxiv.org/abs/2305.18076) (Tao Feng \u0026 Jie Zhang et al., 2023) [:book:](./citations/feng2023hash.txt)\n\n\u003ca name=\"video\" /\u003e\n\n### Video\n+ [Dancing with Still Images: Video Distillation via Static-Dynamic Disentanglement](https://arxiv.org/abs/2312.00362) (Ziyu Wang \u0026 Yue Xu et al., CVPR 2024) [:octocat:](https://github.com/yuz1wan/video_distillation) [:book:](./citations/wang2023dancing.txt)\n+ [Video Set Distillation: Information Diversification and Temporal Densifica](https://arxiv.org/abs/2412.00111) (Yinjie Zhao et al., 2024) [:book:](./citations/zhao2024video.txt)\n+ [A Large-Scale Study on Video Action Dataset Condensation](https://arxiv.org/abs/2412.21197) (Yang Chen et al., 2024) [:octocat:](https://github.com/MCG-NJU/Video-DC) [:book:](./citations/chen2024video.txt)\n+ [Condensing Action Segmentation Datasets via Generative Network Inversion](https://arxiv.org/abs/2503.14112) (Guodong Ding et al., CVPR 2025) [:book:](./citations/ding2025video.txt)\n+ [Latent Video Dataset Distillation](https://arxiv.org/abs/2412.00111) (Ning Li et al., CVPR 2025 Workshop) [:book:](./citations/li2025latent.txt)\n\n\u003ca name=\"domain\" /\u003e\n\n### Domain Adaptation\n+ [Multi-Source Domain Adaptation Meets Dataset Distillation through Dataset Dictionary Learning](https://arxiv.org/abs/2309.07666) (Eduardo Montesuma et al., ICASSP 2024) [:book:](./citations/montesuma2024multi.txt)\n\n\u003ca name=\"super\" /\u003e\n\n### Super Resolution\n+ [GSDD: Generative Space Dataset Distillation for Image Super-resolution](https://ojs.aaai.org/index.php/AAAI/article/view/28534) (Haiyu Zhang et al., AAAI 2024) [:book:](./citations/zhang2024super.txt)\n\n\u003ca name=\"time\" /\u003e\n\n### Time Series\n+ [Dataset Condensation for Time Series Classification via Dual Domain Matching](https://arxiv.org/abs/2403.07245) (Zhanyu Liu et al., KDD 2024) [:octocat:](https://github.com/zhyliu00/TimeSeriesCond) [:book:](./citations/liu2024time.txt)\n+ [CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting](https://arxiv.org/abs/2406.02131) (Jianrong Ding \u0026 Zhanyu Liu et al., NeurIPS 2024) [:octocat:](https://github.com/RafaDD/CondTSF) [:book:](./citations/ding2024time.txt)\n+ [Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching](https://arxiv.org/abs/2410.20905) (Hao Miao et al., VLDB 2025) [:octocat:](https://github.com/uestc-liuzq/STdistillation) [:book:](./citations/miao2025timedc.txt)\n\n\u003ca name=\"speech\" /\u003e\n\n### Speech\n+ [Dataset-Distillation Generative Model for Speech Emotion Recognition](https://arxiv.org/abs/2406.02963) (Fabian Ritter-Gutierrez et al., Interspeech 2024) [:book:](./citations/fabian2024speech.txt)\n\n\u003ca name=\"unlearning\" /\u003e\n\n### Machine Unlearning\n+ [Distilled Datamodel with Reverse Gradient Matching](https://arxiv.org/abs/2404.14006) (Jingwen Ye et al., CVPR 2024) [:book:](./citations/ye2024datamodel.txt)\n+ [Dataset Condensation Driven Machine Unlearning](https://arxiv.org/abs/2402.00195) (Junaid Iqbal Khan, 2024) [:octocat:](https://github.com/algebraicdianuj/DC_U) [:book:](./citations/khan2024unlearning.txt)\n\n\u003ca name=\"rl\" /\u003e\n\n### Reinforcement Learning\n+ [Behaviour Distillation](https://arxiv.org/abs/2406.15042) (Andrei Lupu et al., ICLR 2024) [:octocat:](https://github.com/flairox/behaviour-distillation) [:book:](./citations/lupu2024bd.txt)\n+ [Dataset Distillation for Offline Reinforcement Learning](https://arxiv.org/abs/2407.20299) (Jonathan Light \u0026 Yuanzhe Liu et al., ICML 2024 Workshop) [:globe_with_meridians:](https://datasetdistillation4rl.github.io/) [:octocat:](https://github.com/ggflow123/DDRL) [:book:](./citations/light2024rl.txt)\n+ [Offline Behavior Distillation](https://arxiv.org/abs/2410.22728) (Shiye Lei et al., NeurIPS 2024) [:octocat:](https://github.com/LeavesLei/OBD) [:book:](./citations/lei2024obl.txt)\n\n\u003ca name=\"long\" /\u003e\n\n### Long-Tail\n+ [Distilling Long-tailed Datasets](https://arxiv.org/abs/2408.14506) (Zhenghao Zhao \u0026 Haoxuan Wang et al., CVPR 2025) [:octocat:](https://github.com/ichbill/LTDD) [:book:](./citations/zhao2025long.txt)\n\n\u003ca name=\"noisy\" /\u003e\n\n### Learning with Noisy Labels\n+ [Dataset Distillers Are Good Label Denoisers In the Wild](https://arxiv.org/abs/2411.11924) (Lechao Cheng et al., 2024) [:octocat:](https://github.com/Kciiiman/DD_LNL) [:book:](./citations/cheng2024noisy.txt)\n\n\u003ca name=\"detection\" /\u003e\n\n### Object Detection\n+ [Fetch and Forge: Efficient Dataset Condensation for Object Detection](https://openreview.net/forum?id=m8MElyzuwp) (Ding Qi et al., NeurIPS 2024) [:book:](./citations/qi2024dcod.txt)\n\n\u003ca name=\"point\" /\u003e\n\n### Point Cloud\n+ [Permutation-Invariant and Orientation-Aware Dataset Distillation for 3D Point Clouds](https://arxiv.org/abs/2503.22154) (Jae-Young Yim \u0026 Dongwook Kim et al., 2025) [:book:](./citations/yim2025point.txt)\n\n## Media Coverage\n+ [Beginning of Awesome Dataset Distillation](https://twitter.com/TongzhouWang/status/1560043815204970497?cxt=HHwWgoCz9bPlsaYrAAAA)\n+ [Most Popular AI Research Aug 2022](https://www.libhunt.com/posts/874974-d-most-popular-ai-research-aug-2022-ranked-based-on-github-stars)\n+ [一个项目帮你了解数据集蒸馏Dataset Distillation](https://www.jiqizhixin.com/articles/2022-10-11-22)\n+ [浓缩就是精华：用大一统视角看待数据集蒸馏](https://mp.weixin.qq.com/s/__IjS0_FMpu35X9cNhNhPg)\n\n## Star History\n[![Star History Chart](https://api.star-history.com/svg?repos=Guang000/Awesome-Dataset-Distillation\u0026type=Date)](https://star-history.com/#Guang000/Awesome-Dataset-Distillation\u0026Date)\n\n## Citing Awesome Dataset Distillation\nIf you find this project useful for your research, please use the following BibTeX entry.\n```\n@misc{li2022awesome,\n  author={Li, Guang and Zhao, Bo and Wang, Tongzhou},\n  title={Awesome Dataset Distillation},\n  howpublished={\\url{https://github.com/Guang000/Awesome-Dataset-Distillation}},\n  year={2022}\n}\n```\n\n## Acknowledgments\nWe would like to express our heartfelt thanks to [Nikolaos Tsilivis](https://github.com/Tsili42), [Wei Jin](https://github.com/ChandlerBang), [Yongchao Zhou](https://github.com/yongchao97), [Noveen Sachdeva](https://github.com/noveens), [Can Chen](https://github.com/GGchen1997), [Guangxiang Zhao](https://github.com/zhaoguangxiang), [Shiye Lei](https://github.com/LeavesLei), [Xinchao Wang](https://sites.google.com/site/sitexinchaowang/), [Dmitry Medvedev](https://github.com/dm-medvedev), [Seungjae Shin](https://github.com/SJShin-AI), [Jiawei Du](https://github.com/AngusDujw), [Yidi Jiang](https://github.com/Jiang-Yidi), [Xindi Wu](https://github.com/XindiWu), [Guangyi Liu](https://github.com/lgy0404), [Yilun Liu](https://github.com/superallen13), [Kai Wang](https://github.com/kaiwang960112), [Yue Xu](https://github.com/silicx), [Anjia Cao](https://github.com/CAOANJIA), [Jianyang Gu](https://github.com/vimar-gu), [Yuanzhen Feng](https://github.com/fengyzpku), [Peng Sun](https://github.com/sp12138), [Ahmad Sajedi](https://github.com/AhmadSajedii), [Zhihao Sui](https://github.com/suizhihao), [Ziyu Wang](https://github.com/yuz1wan), [Haoyang Liu](https://github.com/Liu-Hy), [Eduardo Montesuma](https://github.com/eddardd), [Shengbo Gong](https://github.com/rockcor), [Zheng Zhou](https://github.com/zhouzhengqd), [Zhenghao Zhao](https://github.com/ichbill), [Duo Su](https://github.com/suduo94), [Tianhang Zheng](https://github.com/tianzheng4), [Shijie Ma](https://github.com/mashijie1028), [Wei Wei](https://github.com/WeiWeic6222848), [Yantai Yang](https://github.com/Hiter-Q), [Shaobo Wang](https://github.com/gszfwsb), [Xinhao Zhong](https://github.com/ndhg1213), [Zhiqiang Shen](https://github.com/szq0214), [Cong Cong](https://github.com/thomascong121), [Chun-Yin Huang](https://github.com/chunyinhuang), [Dai Liu](https://github.com/NiaLiu), [Ruonan Yu](https://github.com/Lexie-YU), [William Holland](https://github.com/rayneholland), [Saksham Singh Kushwaha](https://github.com/sakshamsingh1), [Ping Liu](https://github.com/pinglmlcv), [Wenliang Zhong](https://github.com/Zhong0x29a), [Ning Li](https://github.com/Ning9319), and [Guochen Yan](https://github.com/Youth-49) for their valuable suggestions and contributions.\n\nThe [Homepage](https://guang000.github.io/Awesome-Dataset-Distillation/) of Awesome Dataset Distillation was designed by [Longzhen Li](https://github.com/LOVELESSG) and maintained by [Mingzhuo Li](https://github.com/SumomoTaku).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FGuang000%2FAwesome-Dataset-Distillation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FGuang000%2FAwesome-Dataset-Distillation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FGuang000%2FAwesome-Dataset-Distillation/lists"}