Awesome-Dataset-Distillation

A curated list of awesome papers on dataset distillation and related applications.
https://github.com/Guang000/Awesome-Dataset-Distillation

Last synced: 3 days ago
JSON representation

Main
- - Dataset Distillation - distillation) [:book:](./citations/wang2018datasetdistillation.txt)
- Early Work
  - Gradient-Based Hyperparameter Optimization Through Reversible Learning
- Gradient/Trajectory Matching Surrogate Objective
  - Dataset Condensation with Gradient Matching - UoE/DatasetCondensation) [:book:](./citations/zhao2021datasetcondensation.txt)
  - Dataset Condensation with Differentiable Siamese Augmentation - UoE/DatasetCondensation) [:book:](./citations/zhao2021differentiatble.txt)
  - Dataset Distillation by Matching Training Trajectories - distillation/) [:octocat:](https://github.com/georgecazenavette/mtt-distillation) [:book:](./citations/cazenavette2022dataset.txt)
  - Dataset Condensation with Contrastive Signals - lee/dcc) [:book:](./citations/lee2022dataset.txt)
  - Delving into Effective Gradient Matching for Dataset Condensation
  - Loss-Curvature Matching for Dataset Selection and Condensation - AI/LCMat) [:book:](./citations/shin2023lcmat.txt)
  - Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation - distillation) [:book:](./citations/du2023minimizing.txt)
  - Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory
  - Sequential Subset Matching for Dataset Distillation
  - Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching
  - Neural Spectral Decomposition for Dataset Distillation
  - SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching
  - Prioritize Alignment in Dataset Distillation - HPC-AI-Lab/PAD) [:book:](./citations/li2024pad.txt)
  - Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios - HPC-AI-Lab/EDF) [:book:](./citations/wang2025edf.txt)
  - SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching
  - Dataset Distillation by Automatic Training Trajectories
  - Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory
- Distribution/Feature Matching Surrogate Objective
  - CAFE: Learning to Condense Dataset by Aligning Features
  - Dataset Condensation with Distribution Matching - UoE/DatasetCondensation) [:book:](./citations/zhao2023distribution.txt)
  - Improved Distribution Matching for Dataset Condensation
  - DataDAM: Efficient Dataset Distillation with Attention Matching
  - M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy - Zhang/M3D) [:book:](./citations/zhang2024m3d.txt)
  - Dataset Condensation with Latent Quantile Matching
  - Dataset Distillation via the Wasserstein Metric
  - Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation
  - Diversified Semantic Distribution Matching for Dataset Distillation
  - Diversified Semantic Distribution Matching for Dataset Distillation - Hongcheng/DSDM) [:book:](./citations/li2024dsdm.txt)
  - DANCE: Dual-View Distribution Alignment for Dataset Condensation - Zhang/DANCE) [:book:](./citations/zhang2024dance.txt)
  - Dataset Distillation with Neural Characteristic Function: A Minmax Perspective
- Kernel-Based Distillation
  - Dataset Meta-Learning from Kernel Ridge-Regression - tangents) [:book:](./citations/nguyen2021kip.txt)
  - Dataset Distillation with Infinitely Wide Convolutional Networks - tangents) [:book:](./citations/nguyen2021kipimprovedresults.txt)
  - Dataset Distillation using Neural Feature Regression
  - Efficient Dataset Distillation using Random Feature Approximation
  - Dataset Distillation with Convexified Implicit Gradients
  - Provable and Efficient Dataset Distillation for Kernel Ridge Regression
- Better Optimization
  - Accelerating Dataset Distillation via Model Augmentation - dk-lab/Acc-DD) [:book:](./citations/zhang2023accelerating.txt)
  - DREAM: Efficient Dataset Distillation by Representative Matching
  - MIM4DD: Mutual Information Maximization for Dataset Distillation
  - Can Pre-Trained Models Assist in Dataset Distillation? - zjut/DDInterpreter) [:book:](./citations/lu2023pre.txt)
  - DREAM+: Efficient Dataset Distillation by Bidirectional Representative Matching
  - Embarassingly Simple Dataset Distillation
  - Dataset Distillation in Latent Space
  - Distill Gold from Massive Ores: Bi-level Data Pruning towards Efficient Dataset Distillation
  - BACON: Bayesian Optimal Condensation Framework for Dataset Distillation
  - You Only Condense Once: Two Rules for Pruning Condensed Datasets - y/you-only-condense-once) [:book:](./citations/he2023yoco.txt)
  - Large Scale Dataset Distillation with Domain Shift
  - Towards Model-Agnostic Dataset Condensation by Heterogeneous Models - Yeong Moon et al., ECCV 2024) [:octocat:](https://github.com/khu-agi/hmdc) [:book:](./citations/moon2024hmdc.txt)
  - Teddy: Efficient Large-Scale Dataset Distillation via Taylor-Approximated Matching
  - Multisize Dataset Condensation - y/Multisize-Dataset-Condensation) [:book:](./citations/he2024mdc.txt)
  - Curriculum Coarse-to-Fine Selection for High-IPC Dataset Distillation
  - Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality - Group/ProgressiveDD) [:book:](./citations/chen2024vodka.txt)
  - Going Beyond Feature Similarity: Effective Dataset Distillation based on Class-aware Conditional Mutual Information
- Better Understanding
  - On the Size and Approximation Error of Distilled Sets
  - Dataset Distillation as Pushforward Optimal Quantization
  - Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning
  - Mitigating Bias in Dataset Distillation
  - What is Dataset Distillation Learning? - is-Dataset-Distillation-Learning) [:book:](./citations/yang2024learning.txt)
  - A Theoretical Study of Dataset Distillation
  - Optimizing Millions of Hyperparameters by Implicit Differentiation - in-100-Lines-of-Code/tree/main/Optimizing_Millions_of_Hyperparameters_by_Implicit_Differentiation) [:book:](./citations/lorraine2020optimizing.txt)
  - On Implicit Bias in Overparameterized Bilevel Optimization
  - Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation
- Decoupled Distillation
  - Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective - Lab/SRe2L/tree/main/SRe2L) [:book:](./citations/yin2023sre2l.txt)
  - Dataset Distillation via Committee Voting - DD) [:book:](./citations/cui2025cvdd.txt)
  - FocusDD: Real-World Scene Infusion for Robust Dataset Distillation
  - Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment - Driven-Synthesis) [:book:](./citations/du2024diversity.txt)
  - On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm - lab/RDED) [:book:](./citations/sun2024rded.txt)
  - Elucidating the Design Space of Dataset Condensation
  - Dataset Distillation via Curriculum Data Synthesis in Large Data Era - Lab/SRe2L/tree/main/CDA) [:book:](./citations/yin2024cda.txt)
  - Information Compensation: A Fix for Any-scale Dataset Distillation
  - Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching
  - DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation - Lab/DELT) [:book:](./citations/shen2025delt.txt)
  - Enhancing Dataset Distillation via Non-Critical Region Refinement - Tuan Tran et al., CVPR 2025) [:octocat:](https://github.com/tmtuan1307/NRR-DD) [:book:](./citations/tran2025nrrdd.txt)
  - Curriculum Dataset Distillation
  - Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator - xd/UFC) [:book:](./citations/zhang2025infer.txt)
- Distilled Dataset Parametrization
  - Dataset Condensation via Efficient Synthetic-Data Parameterization - Hyun Kim et al., ICML 2022) [:octocat:](https://github.com/snu-mllab/efficient-dataset-condensation) [:book:](./citations/kim2022dataset.txt)
  - Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks - DatasetDistillation) [:book:](./citations/deng2022remember.txt)
  - On Divergence Measures for Bayesian Pseudocoresets - divergences) [:book:](./citations/kim2022divergence.txt)
  - Dataset Distillation via Factorization
  - PRANC: Pseudo RAndom Networks for Compacting Deep Models
  - Dataset Condensation with Latent Space Knowledge Factorization and Sharing
  - Slimmable Dataset Condensation
  - Few-Shot Dataset Distillation via Translative Pre-Training
  - Sparse Parameterization for Epitomic Dataset Distillation - XJTU/SPEED) [:book:](./citations/wei2023sparse.txt)
  - Frequency Domain-based Dataset Distillation
  - MGDD: A Meta Generator for Fast Dataset Distillation
  - Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation
  - Distilling Dataset into Neural Field - kaist/DDiF) [:book:](./citations/shin2025ddif.txt)
  - Color-Oriented Redundancy Reduction in Dataset Distillation
  - Color-Oriented Redundancy Reduction in Dataset Distillation
  - FYI: Flip Your Images for Dataset Distillation - yonsei/FYI) [:book:](./citations/son2024fyi.txt)
  - Distilling Dataset into Neural Field
- Label Distillation
  - Flexible Dataset Distillation: Learn Labels Instead of Images - distillation) [:book:](./citations/bohdal2020flexible.txt)
  - Soft-Label Dataset Distillation and Text Dataset Distillation - distillation) [:book:](./citations/sucholutsky2021soft.txt)
  - Label-Augmented Dataset Distillation
  - A Label is Worth a Thousand Images in Dataset Distillation - distillation) [:book:](./citations/qin2024label.txt)
  - DRUPI: Dataset Reduction Using Privileged Information
  - Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation? - y/soft-label-pruning-for-dataset-distillation) [:book:](./citations/xiao2024soft.txt)
  - GIFT: Unlocking Full Potential of Labels in Distilled Dataset at Near-zero Cost - lab/GIFT) [:book:](./citations/shang2025gift.txt)
- Dataset Quantization
  - Dataset Quantization - research/Dataset_Quantization) [:book:](./citations/zhou2023dataset.txt)
  - Dataset Quantization with Active Learning based Adaptive Sampling
  - Adaptive Dataset Quantization
- Multimodal Distillation
  - Vision-Language Dataset Distillation
  - Low-Rank Similarity Mining for Multimodal Dataset Distillation
  - Audio-Visual Dataset Distillation
- Self-Supervised Distillation
  - Self-Supervised Dataset Distillation for Transfer Learning - Lee/selfsup_dd) [:book:](./citations/lee2024self.txt)
  - Efficiency for Free: Ideal Data Are Transportable Representations - lab/ReLA) [:book:](./citations/sun2024rela.txt)
  - Self-supervised Dataset Distillation: A Good Compression Is All You Need - Lab/SRe2L/tree/main/SCDD/) [:book:](./citations/zhou2024self.txt)
  - Dataset Distillation via Knowledge Distillation: Towards Efficient Self-Supervised Pre-Training of Deep Networks
  - Boost Self-Supervised Dataset Distillation via Parameterization, Predefined Augmentation, and Approximation - Feng Yu et al., ICLR 2025) [:book:](./citations/yu2025self.txt)
- Benchmark
  - DC-BENCH: Dataset Condensation Benchmark - bench.github.io/) [:octocat:](https://github.com/justincui03/dc_benchmark) [:book:](./citations/cui2022dc.txt)
  - A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness
  - DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation - HUST/DD-RobustBench) [:book:](./citations/wu2025robust.txt)
  - BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation - leaderboard.github.io/) [:octocat:](https://github.com/zhouzhengqd/BEARD/) [:book:](./citations/zhou2024beard.txt)
- Survey
  - Data Distillation: A Survey
  - A Survey on Dataset Distillation: Approaches, Applications and Future Directions - Dataset-Distillation) [:book:](./citations/geng2023survey.txt)
  - A Comprehensive Survey to Dataset Distillation - Dataset-Distillation) [:book:](./citations/lei2023survey.txt)
  - Dataset Distillation: A Comprehensive Review - Dataset-Distillation) [:book:](./citations/yu2023review.txt)
  - The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions
- Ph.D. Thesis
  - Data-efficient Neural Network Training with Dataset Condensation
- Generative Prior
  - D4M: Dataset Distillation via Disentangled Diffusion Model
- Workshop
  - :globe_with_meridians:
- Generative Distillation
  - Dataset Condensation via Generative Model
  - Generative Dataset Distillation Based on Diffusion Model - Dataset-Distillation-Based-on-Diffusion-Model) [:book:](./citations/su2024diffusion.txt)
  - Generative Dataset Distillation: Balancing Global Structure and Local Details
  - Efficient Dataset Distillation via Minimax Diffusion - gu/MinimaxDiffusion) [:book:](./citations/gu2024efficient.txt)
  - Latent Dataset Distillation with Diffusion Models
  - Synthesizing Informative Training Samples with GAN - uoe/it-gan) [:book:](./citations/zhao2022synthesizing.txt)
  - Generalizing Dataset Distillation via Deep Generative Prior
  - DiM: Distilling Dataset into Generative Model - gu/DiM) [:book:](./citations/wang2023dim.txt)
  - D4M: Dataset Distillation via Disentangled Diffusion Model
  - Data-to-Model Distillation: Data-Efficient Learning Framework
  - Hierarchical Features Matter: A Deep Exploration of GAN Priors for Improved Dataset Distillation - GLaD) [:book:](./citations/zhong2025hglad.txt)
  - Influence-Guided Diffusion for Dataset Distillation
- Challenge
  - The First Dataset Distillation Challenge
  - :globe_with_meridians: - Dataset-Distillation-Challenge)
- Ranking
  - :globe_with_meridians: - HPC-AI-Lab/DD-Ranking) [:book:](./citations/li2025ranking.txt)
Applications
- Medical
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Soft-Label Anonymous Gastric X-ray Image Distillation - distillation) [:book:](./citations/li2020soft.txt)
  - Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing - distillation) [:book:](./citations/li2022compressed.txt)
  - Dataset Distillation for Medical Dataset Sharing - distillation) [:book:](./citations/li2023sharing.txt)
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Image Distillation for Safe Data Sharing in Histopathology
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Progressive Trajectory Matching for Medical Dataset Distillation
  - Dataset Distillation in Medical Imaging: A Feasibility Study
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Dataset Distillation for Histopathology Image Classification
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Importance-Aware Adaptive Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - MedSynth: Leveraging Generative Model for Healthcare Data Sharing
  - Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation
- Continual Learning
  - Reducing Catastrophic Forgetting with Learning on Synthetic Data
  - Condensed Composite Memory Continual Learning
  - Distilled Replay: Overcoming Forgetting through Synthetic Samples
  - Sample Condensation in Online Continual Learning
  - Summarizing Stream Data for Memory-Restricted Online Continual Learning - gu/SSD) [:book:](./citations/gu2024ssd.txt)
  - An Efficient Dataset Condensation Plugin and Its Application to Continual Learning - Efficient-Dataset-Condensation-Plugin) [:book:](./citations/yang2023efficient.txt)
- Privacy
  - SecDD: Efficient and Secure Method for Remotely Training Neural Networks
  - Privacy for Free: How does Dataset Condensation Help Privacy?
  - Private Set Generation with Discriminative Information - Set) [:book:](./citations/chen2022privacy.txt)
  - Backdoor Attacks Against Dataset Distillation
  - Differentially Private Kernel Inducing Points (DP-KIP) for Privacy-preserving Data Distillation
  - No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy"
  - Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation
  - Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective - Yu Chung et al., ICLR 2024) [:book:](./citations/chung2024backdoor.txt)
  - Adaptive Backdoor Attacks Against Dataset Distillation for Federated Learning
  - Differentially Private Dataset Condensation
- Federated Learning
  - Federated Learning via Synthetic Data
  - Distilled One-Shot Federated Learning
  - DENSE: Data-Free One-Shot Federated Learning - jayzhang/DENSE) [:book:](./citations/zhang2022dense.txt)
  - FedSynth: Gradient Compression via Synthetic Data in Federated Learning
  - DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
  - Meta Knowledge Condensation for Federated Learning
  - Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments
  - Federated Virtual Learning on Heterogeneous Data with Local-global Distillation - Yin Huang et al., 2023) [:book:](./citations/huang2023federated.txt)
  - One-Shot Collaborative Data Distillation
  - An Aggregation-Free Federated Learning for Tackling Data Heterogeneity
  - DCFL: Non-IID Awareness Dataset Condensation Aided Federated Learning
  - Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents - main) [:book:](./citations/jia2024feddg.txt)
  - Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors - Yin Huang et al., ICML 2024) [:octocat:](https://github.com/ubc-tea/DESA) [:book:](./citations/huang2024desa.txt)
  - FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning - distribution-matching) [:book:](./citations/xiong2023feddm.txt)
  - FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations - Po Wang et al., 2023) [:octocat:](https://github.com/a514514772/fedlap-dp) [:book:](./citations/wang2023fed.txt)
  - FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis - 49/FedVCK_2024) [:book:](./citations/yan2025fedvck.txt)
- Graph Neural Network
  - Graph Condensation for Graph Neural Networks
  - Condensing Graphs via One-Step Gradient Matching - research/DosCond) [:book:](./citations/jin2022condensing.txt)
  - Graph Condensation via Receptive Field Distribution Matching
  - CaT: Balanced Continual Graph Learning with Graph Condensation - CGL) [:book:](./citations/liu2023cat.txt)
  - Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data - zheng/sfgc) [:book:](./citations/zheng2023sfgc.txt)
  - Does Graph Distillation See Like Vision Dataset Counterpart?
  - Mirage: Model-Agnostic Graph Distillation for Graph Classification
  - Graph Distillation with Eigenbasis Matching - tian/GDEM) [:book:](./citations/liu2024gdem.txt)
  - Graph Data Condensation via Self-expressive Graph Structure Reconstruction
  - Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching - hpc-ai-lab/ctrl) [:book:](./citations/zhang2024ctrl.txt)
  - Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching - hpc-ai-lab/geom) [:book:](./citations/zhang2024geom.txt)
  - GC-Bench: A Benchmark Framework for Graph Condensation with New Insights - Melody/GraphSlim) [:book:](./citations/gong2024graphslim.txt)
  - GC-Bench: An Open and Unified Benchmark for Graph Condensation - Bench) [:book:](./citations/sun2024gcbench.txt)
  - A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation - Melody/awesome-graph-reduction) [:book:](./citations/hashemi2024awesome.txt)
  - Graph Condensation: A Survey - condensation-papers) [:book:](./citations/gao2025graph.txt)
  - A Survey on Graph Condensation - Graph-Condensation) [:book:](./citations/xu2024survey.txt)
  - GCondenser: Benchmarking Graph Condensation
  - Kernel Ridge Regression-Based Graph Dataset Distillation
- Fairness
  - Fair Graph Distillation
  - FairDD: Fair Dataset Distillation via Synchronized Matching
- Neural Architecture Search
  - Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data - research/GTN) [:book:](./citations/such2020generative.txt)
  - Learning to Generate Synthetic Training Data using Gradient Matching and Implicit Differentiation - medvedev/efficientdistillation) [:book:](./citations/medvedev2021tabular.txt)
  - Calibrated Dataset Condensation for Faster Hyperparameter Search
- Fashion, Art, and Design
  - Wearable ImageNet: Synthesizing Tileable Textures via Dataset Distillation - distillation/) [:octocat:](https://github.com/georgecazenavette/mtt-distillation) [:book:](./citations/cazenavette2022textures.txt)
  - Learning from Designers: Fashion Compatibility Analysis Via Dataset Distillation
  - Galaxy Dataset Distillation with Self-Adaptive Trajectory Matching - Dataset-Distillation) [:book:](./citations/guan2023galaxy.txt)
- Knowledge Distillation
  - Knowledge Condensation Distillation
- Recommender Systems
  - Infinite Recommendation Networks: A Data-Centric Approach
  - TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation - StarTeam/TD3) [:book:](./citations/zhang2025td3.txt)
  - Gradient Matching for Categorical Data Distillation in CTR Prediction
- Blackbox Optimization
  - Bidirectional Learning for Offline Infinite-width Model-based Optimization
  - Bidirectional Learning for Offline Model-based Biological Sequence Design - ICML2023-Submission) [:book:](./citations/chen2023bidirectional.txt)
- Robustness
  - Rethinking Data Distillation: Do Not Overlook Calibration
  - Towards Trustworthy Dataset Distillation
  - Group Distributionally Robust Dataset Distillation with Risk Minimization
  - Can We Achieve Robustness from Data Alone?
  - Towards Robust Dataset Learning
  - Towards Adversarially Robust Dataset Distillation by Curvature Regularization
- Retrieval
  - Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching
- Text
  - Data Distillation for Text Classification
  - Dataset Distillation with Attention Labels for Fine-tuning BERT - distillation-with-attention-labels) [:book:](./citations/maekawa2023text.txt)
  - Textual Dataset Distillation via Language Model Embedding
  - DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation
  - UniDetox: Universal Detoxification of Large Language Models via Dataset Distillation
  - Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training
  - UniDetox: Universal Detoxification of Large Language Models via Dataset Distillation
- Tabular
  - New Properties of the Data Distillation Method When Working With Tabular Data - medvedev/dataset-distillation) [:book:](./citations/medvedev2020tabular.txt)
- Learning with Noisy Labels
  - Dataset Distillers Are Good Label Denoisers In the Wild
- Reinforcement Learning
  - Dataset Distillation for Offline Reinforcement Learning
  - Distilling Reinforcement Learning into Single-Batch Datasets
  - Behaviour Distillation - distillation) [:book:](./citations/lupu2024bd.txt)
  - Offline Behavior Distillation
- Time Series
  - CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting
  - Dataset Condensation for Time Series Classification via Dual Domain Matching
  - Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching - liuzq/STdistillation) [:book:](./citations/miao2025timedc.txt)
- Machine Unlearning
  - Distilled Datamodel with Reverse Gradient Matching
  - Dataset Condensation Driven Machine Unlearning
- Video
  - Latent Video Dataset Distillation
  - Dancing with Still Images: Video Distillation via Static-Dynamic Disentanglement
  - Latent Video Dataset Distillation
  - A Large-Scale Study on Video Action Dataset Condensation - NJU/Video-DC) [:book:](./citations/chen2024video.txt)
  - Condensing Action Segmentation Datasets via Generative Network Inversion
- Domain Adaptation
  - Multi-Source Domain Adaptation Meets Dataset Distillation through Dataset Dictionary Learning
- Object Detection
  - Fetch and Forge: Efficient Dataset Condensation for Object Detection
- Super Resolution
  - GSDD: Generative Space Dataset Distillation for Image Super-resolution
- Speech
  - Dataset-Distillation Generative Model for Speech Emotion Recognition - Gutierrez et al., Interspeech 2024) [:book:](./citations/fabian2024speech.txt)
- Long-Tail
  - Distilling Long-tailed Datasets
- Point Cloud
  - Permutation-Invariant and Orientation-Aware Dataset Distillation for 3D Point Clouds - Young Yim & Dongwook Kim et al., 2025) [:book:](./citations/yim2025point.txt)
Media Coverage
- Point Cloud
- Reinforcement Learning
  - Beginning of Awesome Dataset Distillation
Acknowledgments
- Point Cloud
  - Homepage
  - Nikolaos Tsilivis - 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.
- Object Detection
  - Zhihao Sui - 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), and [Ping Liu](https://github.com/pinglmlcv) for their valuable suggestions and contributions.
Star History
- Tabular
  - ![Star History Chart - history.com/#Guang000/Awesome-Dataset-Distillation&Date)
- Point Cloud
  - ![Star History Chart - history.com/#Guang000/Awesome-Dataset-Distillation&Date)

Programming Languages

Categories

Applications 189 Main 138 Media Coverage 5 Acknowledgments 3 Star History 2

Sub Categories

Medical 89 Graph Neural Network 18 Distilled Dataset Parametrization 17 Better Optimization 17 Gradient/Trajectory Matching Surrogate Objective 17 Federated Learning 16 Decoupled Distillation 13 Distribution/Feature Matching Surrogate Objective 12 Generative Distillation 12 Privacy 10 Better Understanding 9 Point Cloud 8 Text 7 Label Distillation 7 Kernel-Based Distillation 6 Continual Learning 6 Robustness 6 Video 5 Self-Supervised Distillation 5 Survey 5 Reinforcement Learning 5 Benchmark 4 Multimodal Distillation 3 Fashion, Art, and Design 3 Time Series 3 Recommender Systems 3 Dataset Quantization 3 Neural Architecture Search 3 Object Detection 2 Tabular 2 Challenge 2 Blackbox Optimization 2 Machine Unlearning 2 Fairness 2 Early Work 1 Super Resolution 1 Retrieval 1 Generative Prior 1 Long-Tail 1 Ph.D. Thesis 1 Ranking 1 Speech 1 Knowledge Distillation 1 Learning with Noisy Labels 1 Domain Adaptation 1 Workshop 1

Keywords

deep-learning 1 awesome-list 1