https://github.com/danelpeng/awesome-continual-leaning-with-ptms

This is a curated list of "Continual Learning with Pretrained Models" research.
https://github.com/danelpeng/awesome-continual-leaning-with-ptms

List: awesome-continual-leaning-with-ptms

adapters awesome continual-learning diffusion-models embodied-ai large-language-models mixture-of-experts parameter-efficient-tuning pretrained-models prompt-tuning vision-language-models

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This is a curated list of "Continual Learning with Pretrained Models" research.

• Host: GitHub
• URL: https://github.com/danelpeng/awesome-continual-leaning-with-ptms
• Owner: danelpeng
• License: apache-2.0
• Created: 2024-10-14T03:17:12.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2025-03-24T08:30:02.000Z (2 months ago)
• Last Synced: 2025-03-24T09:36:31.713Z (2 months ago)
• Topics: adapters, awesome, continual-learning, diffusion-models, embodied-ai, large-language-models, mixture-of-experts, parameter-efficient-tuning, pretrained-models, prompt-tuning, vision-language-models
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  • License: LICENSE

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README

        
# 🤖 Awesome-Continual-Learning-with-PTMs

> This is a curated list of "Continual Learning with Pretrained Models" research which is maintained by [danelpeng](https://github.com/danelpeng).

![](https://github.com/danelpeng/Awesome-Continual-Leaning-with-PTMs/blob/main/lifelong-learning.png)

## News 🔥

[2025/03/24] Updated with latest papers.

[2024/10/25] Updated with latest papers.

[2024/10/08] Created this repo.

## Table of Contents 📰

- [Survey](#Survey)

- [Prompt Based](#Prompt-Based)

- [Adapter Based](#Adapter-Based)

- [LoRA Based](#LoRA-Based)

- [MoE/Ensemble Based](#MoEEnsemble-Based)

- [VLM Based](#VLM-Based)

- [Diffusion Based](#Diffusion-Based)

- [Application](#Application)

## Methods 🌟

> ### Survey

* [**When Continue Learning Meets Multimodal Large Language Model: A Survey**](https://arxiv.org/pdf/2503.01887) [**Arxiv 2025.02**]

China Agricultural University, Peking University

* [**Achieving Upper Bound Accuracy of Joint Training in Continual Learning**](https://arxiv.org/pdf/2502.12388) [**Arxiv 2025.02**]

University of Illinois Chicago

* [**Lifelong Learning of Large Language Model based Agents: A Roadmap**](https://arxiv.org/pdf/2501.07278) [**Arxiv 2025.01**]

South China University of Technology, Mohamed bin Zayed University of Artificial Intelligence, Tencent AI Lab

* [**Continual Learning of Large Language Models: A Comprehensive Survey**](https://arxiv.org/abs/2404.16789) [**Arxiv 2024.11**]

Rutgers University, Google Cloud AI Research

* [**Recent Advances of Multimodal Continual Learning: A Comprehensive Survey**](https://arxiv.org/pdf/2410.05352) [**Arxiv 2024.10**]

The Chinese University of Hong Kong, Tsinghua University, University of Illinois Chicago

* [**Towards Lifelong Learning of Large Language Models: A Survey**](https://arxiv.org/pdf/2410.05352) [**Arxiv 2024.06**]

South China University of Technology

* [**Continual Learning for Large Language Models: A Survey**](https://arxiv.org/pdf/2402.01364) [**Arxiv 2024.02**]

Monash University, Griffith University

* [**Continual Learning with Pre-Trained Models: A Survey**](https://arxiv.org/pdf/2401.16386) [**IJCAI 2024**]

Nanjing University

* [**How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances**](https://arxiv.org/pdf/2310.07343) [**EMNLP 2023**]

University of Technology Sydney, University of Liverpool, University of Wollongong, University College London

> ### Prompt Based 

* [**Dynamic Prompt Adjustment for Multi-Label Class-Incremental Learning**](https://arxiv.org/pdf/2501.00340) [**Arxiv 2024.12**]

Anhui University

* [**PEARL: Input-Agnostic Prompt Enhancement with Negative Feedback Regulation for Class-Incremental Learning**](https://arxiv.org/pdf/2412.10900) [**AAAI 2025**]

Southeast University

* [**CAPrompt: Cyclic Prompt Aggregation for Pre-Trained Model Based Class Incremental Learning**](https://arxiv.org/pdf/2412.08929) [**Arxiv 2024.12**]

Peking University

* [**Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective**](https://arxiv.org/pdf/2412.08285) [**AAAI 2025**]

Vin AI Research

* [**Semantic Residual Prompts for Continual Learning**](https://arxiv.org/pdf/2403.06870) [**ECCV 2024**]

University of Modena and Reggio Emilia

* [**Dynamically Managing a Prompt Pool via Self-Enhancement in Continual Learning**](https://openreview.net/pdf?id=GI6gHATAMt) [**NeurIPS 2024**]

Chung-Ang University, German Research Center for Artificial Intelligence

* [**Vector Quantization Prompting for Continual Learning**](https://arxiv.org/pdf/2410.20444) [**Arxiv 2024.10**]

Communication University of China, Harbin Institute of Technology, Shenzhen, The Chinese University of Hong Kong

* [**Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach**](https://arxiv.org/pdf/2410.10341) [**NeurIPS 2024**]

University of Technology Sydney, Singapore Management University, University of Illinois at Chicago

* [**ModalPrompt:Dual-Modality Guided Prompt for Continual Learning of Large Multimodal Models**](https://arxiv.org/pdf/2410.05849) [**Arxiv 2024.10**]

Institute of Automation, CAS

* [**Leveraging Hierarchical Taxonomies in Prompt-based Continual Learning**](https://arxiv.org/pdf/2410.04327) [**Arxiv 2024.10**]

VinAI Research, Monash University, Hanoi University of Science and Technolgy, Univesity of Oregon, The University of Texas at Austin

* [**LW2G: Learning Whether to Grow for Prompt-based Continual Learning**](https://arxiv.org/pdf/2409.18860) [**Arxiv 2024.09**]

Zhejiang University, Nanjing University

* [**Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models**](https://arxiv.org/pdf/2407.05342) [**ECCV 2024**]

Tsinghua University, SmartMore, CUHK, HIT(SZ), Meta Reality Labs, HKU

* [**Evolving Parameterized Prompt Memory for Continual Learning**](https://ojs.aaai.org/index.php/AAAI/article/view/29231/30323) [**AAAI 2024**]

Xi'an Jiaotong University

* [**Generating Prompts in Latent Space for Rehearsal-free Continual Learning**](https://openreview.net/pdf?id=6HT4jUkSRg) [**ACMMM 2024**]

East China Normal University

* [**Convolutional Prompting meets Language Models for Continual Learning**](https://openaccess.thecvf.com/content/CVPR2024/papers/Roy_Convolutional_Prompting_meets_Language_Models_for_Continual_Learning_CVPR_2024_paper.pdf) [**CVPR 2024**]

IIT Kharagpur, IML Amazon India

* [**Consistent Prompting for Rehearsal-Free Continual Learning**](https://openaccess.thecvf.com/content/CVPR2024/papers/Gao_Consistent_Prompting_for_Rehearsal-Free_Continual_Learning_CVPR_2024_paper.pdf) [**CVPR 2024**]

Sun Yat-sen University, HKUST

* [**Steering Prototypes with Prompt-tuning for Rehearsal-free Continual Learning**](https://openaccess.thecvf.com/content/WACV2024/papers/Li_Steering_Prototypes_With_Prompt-Tuning_for_Rehearsal-Free_Continual_Learning_WACV_2024_paper.pdf) [**WACV 2024**]

  Rutgers University, Google Research, Google Cloud AI

  

* [**Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality**](https://proceedings.neurips.cc/paper_files/paper/2023/file/d9f8b5abc8e0926539ecbb492af7b2f1-Paper-Conference.pdf) [**NeurIPS 2023**]

Tsinghua-Bosch Joint Center for ML, Tsinghua University

* [**When Prompt-based Incremental Learning Does Not Meet Strong Pretraining**](https://openaccess.thecvf.com/content/ICCV2023/papers/Tang_When_Prompt-based_Incremental_Learning_Does_Not_Meet_Strong_Pretraining_ICCV_2023_paper.pdf) [**ICCV 2023**]

Sun Yat-sen University, Peng Cheng Laboratory

* [**Introducing Language Guidance in Prompt-based Continual Learning**](https://openaccess.thecvf.com/content/ICCV2023/papers/Khan_Introducing_Language_Guidance_in_Prompt-based_Continual_Learning_ICCV_2023_paper.pdf) [**ICCV 2023**]

RPTU, DFKI, ETH Zurich, TUM, Google

* [**Efficient Continual Pre-training for Building Domain Specific Large Language Models**](https://arxiv.org/pdf/2311.08545) [**Arxiv 2023.10**]

UIUC, Amazon

* [**MoP-CLIP: A Mixture of Prompt-Tuned CLIP Models for Domain Incremental Learning**](https://arxiv.org/pdf/2307.05707) [**Arxiv 2023.07**]

ETS Montreal

* [**Progressive Prompts: Continual Learning for Language Models**](https://arxiv.org/pdf/2301.12314) [**ICLR 2023**]

University of Toronto & Vector Institute, Meta AI

* [**Online Class Incremental Learning on Stochastic Blurry Task Boundary via Mask and Visual Prompt Tuning**](https://openaccess.thecvf.com/content/ICCV2023/papers/Moon_Online_Class_Incremental_Learning_on_Stochastic_Blurry_Task_Boundary_via_ICCV_2023_paper.pdf) [**ICCV 2023**]

Kyung Hee University

* [**Self-regulating Prompts: Foundational Model Adaptation without Forgetting**](https://openaccess.thecvf.com/content/ICCV2023/papers/Khattak_Self-regulating_Prompts_Foundational_Model_Adaptation_without_Forgetting_ICCV_2023_paper.pdf) [**ICCV 2023**]

Mohamed bin Zayed University of AI, Australian National University, Linkoping University, University of California, Merced, Google Research

* [**Generating Instance-level Prompts for Rehearsal-free Continual Learning**](https://openaccess.thecvf.com/content/ICCV2023/html/Jung_Generating_Instance-level_Prompts_for_Rehearsal-free_Continual_Learning_ICCV_2023_paper.html) [**ICCV 2023 (oral)**]

Seoul National University, NAVER AI Lab, NAVER Cloud, AWS AI Labs

* [**CODA-Prompt: COntinual Decomposed Attention-Based Prompting for Rehearsal-Free Continual Learning**](https://openaccess.thecvf.com/content/CVPR2023/papers/Smith_CODA-Prompt_COntinual_Decomposed_Attention-Based_Prompting_for_Rehearsal-Free_Continual_Learning_CVPR_2023_paper.pdf) [**CVPR 2023**]

Georgia Institute of Technology, MIT-IBM Watson AI Lab, Rice University, IBM Research

* [**S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor for Domain Incremental Learning**](https://proceedings.neurips.cc/paper_files/paper/2022/file/25886d7a7cf4e33fd44072a0cd81bf30-Paper-Conference.pdf) [**NeurIPS 2022**]

Xi’an Jiaotong University

* [**DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning**](https://arxiv.org/pdf/2204.04799) [**ECCV 2022**]

Northeastern University, Google Cloud AI, Google Research

* [**Learning to Prompt for Continual Learning**](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Learning_To_Prompt_for_Continual_Learning_CVPR_2022_paper.pdf) [**CVPR 2022**]

Northeastern University, Google Cloud AI, Google Research

> ### Adapter Based 

* [**CMoA: Contrastive Mixture of Adapters for Generalized Few-Shot Continual Learning**](https://ieeexplore.ieee.org/abstract/document/10891550/authors#authors) [**TMM 2025**]

University of Oulu, TeleAI, 

* [**Adapter Merging with Centroid Prototype Mapping for Scalable Class-Incremental Learning**](https://arxiv.org/pdf/2412.18219) [**Arxiv 2024.12**]

Chiba University

* [**Linked Adapters: Linking Past and Future to Present for Effective Continual Learning**](https://arxiv.org/pdf/2412.10687) [**Arxiv 2024.12**]

Indian Institute of Technology Hyderabad, Swinburne University of Technology

* [**Adapter-Enhanced Semantic Prompting for Continual Learning**](https://arxiv.org/pdf/2412.11074) [**Arxiv 2024.12**]

Beijing University of Technology, Macquarie University, University of California at Merced

* [**MOS: Model Surgery for Pre-Trained Model-Based Class-Incremental Learning**](https://arxiv.org/pdf/2412.09441) [**AAAI 2025**]

Nanjing University

* [**Multilingual Continual Learning using Attention Distillation**](https://assets.amazon.science/63/94/0ceaeea44f989d5966c863204398/multilingual-continual-learning-using-attention-distillation.pdf) [**HTML 2024**]

Amazon, India

* [**HyperAdapter: Generating Adapters for Pre-Trained Model-Based Continual Learning**](https://openreview.net/pdf?id=29sul3tAEa) [**Openreview 2024.10**]

Paper under double-blind review

* [**ATLAS: Adapter-Based Multi-Modal Continual Learning with a Two-Stage Learning Strategy**](https://arxiv.org/abs/2410.10923) [**Arxiv 2024.10**]

Shanghai Jiao Tong University,  ShanghaiTech University, Tsinghua University

* [**Adaptive Adapter Routing for Long-Tailed Class-Incremental Learning**](https://arxiv.org/pdf/2409.07446) [**Arxiv 2024.09**]

Nanjing University

* [**Learning to Route for Dynamic Adapter Composition in Continual Learning with Language Models**](https://arxiv.org/pdf/2408.09053) [**Arxiv 2024.08**]

KU Leuven

* [**Expand and Merge: Continual Learning with the Guidance of Fixed Text Embedding Space**](https://ieeexplore.ieee.org/abstract/document/10650910) [**IJCNN 2024**]

Sun Yat-sen University

* [**Beyond Prompt Learning: Continual Adapter for Efficient Rehearsal-Free Continual Learning**](https://arxiv.org/pdf/2407.10281) [**ECCV 2024**]

Xi’an Jiaotong University

* [**Semantically-Shifted Incremental Adapter-Tuning is A Continual ViTransformer**](https://openaccess.thecvf.com/content/CVPR2024/papers/Tan_Semantically-Shifted_Incremental_Adapter-Tuning_is_A_Continual_ViTransformer_CVPR_2024_paper.pdf) [**CVPR 2024**]

Huazhong University of Science and Tech., DAMO Academy, Alibaba Group

* [**Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning**](https://openaccess.thecvf.com/content/CVPR2024/papers/Zhou_Expandable_Subspace_Ensemble_for_Pre-Trained_Model-Based_Class-Incremental_Learning_CVPR_2024_paper.pdf) [**CVPR 2024**]

Nanjing University

> ### LoRA Based 

* [**S-LoRA: Scalable Low-Rank Adaptation for Class Incremental Learning**](https://arxiv.org/pdf/2501.13198) [**Arxiv 2025.01**]

City University of Hong Kong, Harvard University, Xi’an Jiaotong University, Tencent AI Lab, Zhejiang University

* [**Adaptive Rank, Reduced Forgetting: Knowledge Retention in Continual Learning Vision-Language Models with Dynamic Rank-Selective LoRA**](https://arxiv.org/pdf/2412.01004) [**Arxiv 2024.12**]

University of New South Wales, CSIRO’s Data61

* [**DESIRE: Dynamic Knowledge Consolidation for Rehearsal-Free Continual Learning**](https://arxiv.org/pdf/2411.19154) [**Arxiv 2024.11**]

MAIS, Institute of Automation, Chinese Academy of Sciences

* [**Multi-LoRA continual learning based instruction tuning framework for universal information extraction**](https://www.sciencedirect.com/science/article/pii/S0950705124013844) [**Knowledge-Based Systems 2025**]

Nankai University

* [**Dual Low-Rank Adaptation for Continual Learning with Pre-Trained Models**](https://citychan.github.io/assets/publications/2024_arxiv1/paper.pdf) [**Arxiv 2024.10**]

University of Texas at Austin, SonyAI

* [**InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning**](https://openaccess.thecvf.com/content/CVPR2024/papers/Liang_InfLoRA_Interference-Free_Low-Rank_Adaptation_for_Continual_Learning_CVPR_2024_paper.pdf) [**CVPR 2024**]

Nanjing University

* [**Online-LoRA: Task-free Online Continual Learning via Low Rank Adaptation**](https://openreview.net/pdf?id=X7OKRr09OS) [**NeurIPSW 2024**]

University of Texas at Austin

* [**Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters**](https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_Boosting_Continual_Learning_of_Vision-Language_Models_via_Mixture-of-Experts_Adapters_CVPR_2024_paper.pdf) [**CVPR 2024**]

Dalian University of Technology, UESTC, Tsinghua University

* [**Continual learning with low rank adaptation**](https://arxiv.org/pdf/2311.17601) [**NeurIPSW 2023**]

Amazon Web Services

> ### MoE/Ensemble Based 

* [**A scalable Bayesian continual learning framework for online and sequential decision making**](https://openreview.net/pdf?id=NDaaCaWS9N) [**NeurIPSW 2024**]

University of Oxford

* [**CAPrompt: Cyclic Prompt Aggregation for Pre-Trained Model Based Class Incremental Learning**](https://arxiv.org/pdf/2412.08929) [**Arxiv 2024.12**]

Peking University

* [**Learning Attentional Mixture of LoRAs for Language Model Continual Learning**](https://arxiv.org/pdf/2409.19611) [**Arxiv 2024.09**]

Nankai University

* [**Theory on Mixture-of-Experts in Continual Learning**](https://arxiv.org/pdf/2406.16437) [**Arxiv 2024.10**]

Singapore University of Technology and Design, University of Houston, The Ohio State University

* [**Weighted Ensemble Models Are Strong Continual Learners**](https://arxiv.org/abs/2312.08977) [**ECCV 2024**]

Télécom-Paris, Institut Polytechnique de Paris

* [**MagMax: Leveraging Model Merging for Seamless Continual Learning**](https://arxiv.org/pdf/2407.06322) [**ECCV 2024**]

IDEAS NCBR, Warsaw University of Technology

* [**Continual Learning with Weight Interpolation**](https://openaccess.thecvf.com/content/CVPR2024W/CLVISION/papers/Kozal_Continual_Learning_with_Weight_Interpolation_CVPRW_2024_paper.pdf) [**CVPR 2024**]

Wrocław University of Science and Technology, Rochester Institute of Technology

* [**LEMoE: Advanced Mixture of Experts Adaptor for Lifelong Model Editing of Large Language Models**](https://arxiv.org/pdf/2406.20030) [**Arxiv 2024.06**]

Nanjing University of Aeronautics and Astronautics

* [**Mixture of Experts Meets Prompt-Based Continual Learning**](https://arxiv.org/pdf/2405.14124) [**Arxiv 2024.05**]

The University of Texas at Austin, Hanoi University of Science and Technology, VinAI Research

* [**Learning More Generalized Experts by Merging Experts in Mixture-of-Experts**](https://arxiv.org/pdf/2405.11530) [**Arxiv 2024.05**]

KAIST

* [**MoRAL: MoE Augmented LoRA for LLMs’ Lifelong Learning**](https://arxiv.org/pdf/2402.11260) [**Arxiv 2024.02**]

Provable Responsible AI and Data Analytics (PRADA) Lab, KAUST, University of Macau

* [**Divide and not forget: Ensemble of selectively trained experts in Continual Learning**](https://arxiv.org/pdf/2401.10191) [**ICLR 2024**]

IDEAS-NCBR, Warsaw University of Technology

* [**Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters**](https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_Boosting_Continual_Learning_of_Vision-Language_Models_via_Mixture-of-Experts_Adapters_CVPR_2024_paper.pdf) [**CVPR 2024**]

Dalian University of Technology, UESTC, Tsinghua University

* [**An Efficient General-Purpose Modular Vision Model via Multi-Task Heterogeneous Training**](https://arxiv.org/pdf/2306.17165) [**Arxiv 2023.06**]

University of Massachusetts Amherst, University of California Berkeley, MIT-IBM Watson AI Lab

* [**Lifelong Language Pretraining with Distribution-Specialized Experts**](https://proceedings.mlr.press/v202/chen23aq/chen23aq.pdf) [**ICML 2023**]

The University of Texas at Austin, Google

* [**Continual Learning Beyond a Single Model**](https://proceedings.mlr.press/v232/doan23a/doan23a.pdf) [**CoLLAs 2023**]

Bosch Center for Artificial Intelligence, Washington State University, Apple

* [**Mixture-of-Variational-Experts for Continual Learning**](https://arxiv.org/pdf/2009.04381) [**Arxiv 2022.03**]

Ulm University

* [**CoSCL: Cooperation of Small Continual Learners is Stronger Than a Big One**](https://arxiv.org/pdf/2207.06543) [**ECCV 2022**]

Tsinghua University

* [**Ex-Model: Continual Learning from a Stream of Trained Models**](https://openaccess.thecvf.com/content/CVPR2022W/CLVision/papers/Carta_Ex-Model_Continual_Learning_From_a_Stream_of_Trained_Models_CVPRW_2022_paper.pdf) [**CVPRW 2022**]

University of Pisa

* [**Model Zoo: A Growing "Brain" That Learns Continually**](https://arxiv.org/pdf/2106.03027) [**ICLR 2022**]

University of Pennsylvania

* [**Routing Networks with Co-training for Continual Learning**](https://arxiv.org/pdf/2009.04381) [**ICMLW 2020**]

Google AI, Zurich

* [**A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning**](https://arxiv.org/pdf/2001.00689) [**ICLR 2020**]

Seoul National University

* [**Continual Learning in Task-Oriented Dialogue Systems**](https://arxiv.org/pdf/2012.15504) [**Arxiv 2020.12**]

HKUST, Facebook

> ### VLM Based 

* [**Enhanced Continual Learning of Vision-Language Models with Model Fusion**](https://www.arxiv.org/abs/2503.10705) [**ICLRW 2025**]

Shanghai Jiao Tong University, Tencent

* [**Visual Class Incremental Learning with Textual Priors Guidance based on an Adapted Vision-Language Model**](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10897910) [**TMM 2025**]

Sun Yat-sen Univerisity

* [**Efficient Few-Shot Continual Learning in Vision-Language Models**](https://arxiv.org/pdf/2502.04098) [**Arxiv 2025.02**]

University of Cambridge, Toyota Motor Europe

* [**Differentiable Prompt Learning for Vision Language Models**](https://arxiv.org/pdf/2501.00457) [**Arxiv 2024.12**]

Rensselaer Polytechnic Institute, IBM Research

* [**How to Merge Your Multimodal Models Over Time?**](https://arxiv.org/pdf/2412.06712) [**Arxiv 2024.12**]

University of T ¨ ubingen, University of Cambridge, Technical University of Munich

* [**Exemplar Masking for Multimodal Incremental Learning**](https://arxiv.org/pdf/2412.09549) [**Arxiv 2024.12**]

National Yang Ming Chiao Tung University, Google

* [**Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling**](https://arxiv.org/pdf/2412.07077) [**WACV 2025**]

Seoul National University

* [**Continual learning with task specialist**](https://arxiv.org/pdf/2409.17806) [**Arxiv 2024.09**]

International Institute of Information Technology Bangalore, A*STAR

* [**A Practitioner’s Guide to Continual Multimodal Pretraining**](https://arxiv.org/abs/2408.14471) [**NeurIPS 2024**]

University of T¨ubingen, Helmholtz Munich, Munich Center for ML, Google DeepMind

* [**CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models**](https://arxiv.org/pdf/2403.19137) [**NeurIPS 2024**]

UNSW Sydney, CSIRO’s Data61

* [**Stabilizing Zero-Shot Prediction: A Novel Antidote to Forgetting in Continual Vision-Language Tasks**](https://openreview.net/pdf?id=C4zmR2kyP8) [**NeurIPS 2024**]

National University of Defense Technology, Tsinghua University

* [**CLIP with Generative Latent Replay: a Strong Baseline for Incremental Learning**](https://arxiv.org/abs/2407.15793) [**BMVC 2024**]

University of Modena and Reggio

* [**Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models**](https://arxiv.org/pdf/2407.05342) [**ECCV 2024**]

Tsinghua University, SmartMore, CUHK, HIT(SZ), Meta Reality Labs, HKU

* [**Anytime Continual Learning for Open Vocabulary Classification**](https://arxiv.org/abs/2409.08518) [**ECCV 2024 (oral)**]

University of Illinois at Urbana-Champaign

* [**Select and Distill: Selective Dual-Teacher Knowledge Transfer for Continual Learning on Vision-Language Models**](https://arxiv.org/pdf/2403.09296) [**ECCV 2024**]

National Taiwan University, NVIDIA

* [**Adapt without Forgetting: Distill Proximity from Dual Teachers in Vision-Language Models**](https://fq.pkwyx.com/default/https/www.ecva.net/papers/eccv_2024/papers_ECCV/papers/07052.pdf) [**ECCV 2024**]

The University of Sydney, Huawei Noah’s Ark Lab

* [**Class-Incremental Learning with CLIP: Adaptive Representation Adjustment and Parameter Fusion**](https://arxiv.org/pdf/2407.14143) [**ECCV 2024**]

Nankai University

* [**Semantic Residual Prompts for Continual Learning**](https://arxiv.org/pdf/2403.06870) [**ECCV 2024**]

University of Modena and Reggio Emilia

* [**Expand and Merge: Continual Learning with the Guidance of Fixed Text Embedding Space**](https://ieeexplore.ieee.org/abstract/document/10650910) [**IJCNN 2024**]

Sun Yat-sen University

* [**CoLeCLIP: Open-Domain Continual Learning via Joint Task Prompt and Vocabulary Learning**](https://arxiv.org/pdf/2403.10245) [**Arxiv 2024.05**]

Northwestern Polytechnical University, Singapore Management University, Zhejiang University, University of Adelaide

* [**TiC-CLIP: Continual Training of CLIP Models**](https://arxiv.org/pdf/2310.16226) [**ICLR 2024**]

Apple, Carnegie Mellon University

* [**Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters**](https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_Boosting_Continual_Learning_of_Vision-Language_Models_via_Mixture-of-Experts_Adapters_CVPR_2024_paper.pdf) [**CVPR 2024**]

Dalian University of Technology, UESTC, Tsinghua University

* [**Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners**](https://openaccess.thecvf.com/content/CVPR2024/papers/Park_Pre-trained_Vision_and_Language_Transformers_Are_Few-Shot_Incremental_Learners_CVPR_2024_paper.pdf) [**CVPR 2024**]

Kyung Hee University, Yonsei University

* [**Class Incremental Learning with Pre-trained Vision-Language Models**](https://arxiv.org/pdf/2310.20348) [**Arxiv 2023.10**]

Nankai University, University of Florence

* [**MoP-CLIP: A Mixture of Prompt-Tuned CLIP Models for Domain Incremental Learning**](https://arxiv.org/pdf/2307.05707) [**Arxiv 2023.07**]

ETS Montreal

* [**Learning without Forgetting for Vision-Language Models**](https://arxiv.org/pdf/2305.19270) [**Arxiv 2023.05**]

Nanjing University, Nanyang Technological University

* [**Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models**](https://openaccess.thecvf.com/content/ICCV2023/papers/Zheng_Preventing_Zero-Shot_Transfer_Degradation_in_Continual_Learning_of_Vision-Language_Models_ICCV_2023_paper.pdf) [**ICCV 2023**]

National University of Singapore, UC Berkeley, The Chinese University of Hong Kong

* [**Self-regulating Prompts: Foundational Model Adaptation without Forgetting**](https://openaccess.thecvf.com/content/ICCV2023/papers/Khattak_Self-regulating_Prompts_Foundational_Model_Adaptation_without_Forgetting_ICCV_2023_paper.pdf) [**ICCV 2023**]

Mohamed bin Zayed University of AI, Australian National University, Linkoping University, University of California, Merced, Google Research

* [**Introducing Language Guidance in Prompt-based Continual Learning**](https://openaccess.thecvf.com/content/ICCV2023/papers/Khan_Introducing_Language_Guidance_in_Prompt-based_Continual_Learning_ICCV_2023_paper.pdf) [**ICCV 2023**]

RPTU, DFKI, ETH Zurich, TUM, Google

* [**Continual Vision-Language Representation Learning with Off-Diagonal Information**](https://proceedings.mlr.press/v202/ni23c/ni23c.pdf) [**ICML 2023**]

Zhejiang University, Huawei Cloud

* [**CLIP model is an Efficient Continual Learner**](https://arxiv.org/pdf/2210.03114) [**Arxiv 2022.10**]

Mohamed bin Zayed University of Artificial Intelligence, Australian National University, Monash University, Linkoping University

* [**Don’t Stop Learning: Towards Continual Learning for the CLIP Model**](https://arxiv.org/pdf/2207.09248) [**Arxiv 2022.07**]

Xidian University, University of Adelaide

* [**CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks**](https://proceedings.neurips.cc/paper_files/paper/2022/file/bd3611971089d466ab4ca96a20f7ab13-Paper-Datasets_and_Benchmarks.pdf) [**NeurIPS 2022**]

University of Southern California,

* [**S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor for Domain Incremental Learning**](https://proceedings.neurips.cc/paper_files/paper/2022/file/25886d7a7cf4e33fd44072a0cd81bf30-Paper-Conference.pdf) [**NeurIPS 2022**]

Xi’an Jiaotong University

* [**Robust Fine-Tuning of Zero-Shot Models**](https://openaccess.thecvf.com/content/CVPR2022/papers/Wortsman_Robust_Fine-Tuning_of_Zero-Shot_Models_CVPR_2022_paper.pdf) [**CVPR 2022**]

University of Washington, OpenAI, Columbia University, Google Research, Brain Team

> ### Diffusion Based 

* [**Continual learning with task specialist**](https://arxiv.org/pdf/2409.17806) [**Arxiv 2024.09**]

International Institute of Information Technology Bangalore, A*STAR

* [**Diffusion Model Meets Non-Exemplar Class-Incremental Learning and Beyond**](https://arxiv.org/pdf/2409.01128) [**Arxiv 2024.08**]

BNRist, Tsinghua University

* [**Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning**](https://arxiv.org/pdf/2312.06710) [**Arxiv 2024.03**]

VinAI Research, Monash University

* [**Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning**](https://arxiv.org/abs/2408.02983) [**ECCV 2024 (oral)**]

South China University of Technology, HKUST, China University of Petroleum, WeBank, Pazhou Laboratory

* [**DiffClass: Diffusion-Based Class Incremental Learning**](https://arxiv.org/pdf/2403.05016) [**ECCV 2024**]

Northeastern University, ETH Zürich

* [**GUIDE: Guidance-based Incremental Learning with Diffusion Models**](https://arxiv.org/pdf/2403.03938) [**Arxiv 2024.03**]

Warsaw University of Technology

* [**SDDGR: Stable Diffusion-based Deep Generative Replay for Class Incremental Object Detection**](https://openaccess.thecvf.com/content/CVPR2024/papers/Kim_SDDGR_Stable_Diffusion-based_Deep_Generative_Replay_for_Class_Incremental_Object_CVPR_2024_paper.pdf) [**CVPR 2024**]

UNIST, LG Electronics, KETI

* [**Class-Incremental Learning using Diffusion Model for Distillation and Replay**](https://openaccess.thecvf.com/content/ICCV2023W/VCL/papers/Jodelet_Class-Incremental_Learning_Using_Diffusion_Model_for_Distillation_and_Replay_ICCVW_2023_paper.pdf) [**ICCVW 2023**]

Tokyo Institute of Technology, Artificial Intelligence Research Center

* [**DDGR: Continual Learning with Deep Diffusion-based Generative Replay**](https://proceedings.mlr.press/v202/gao23e/gao23e.pdf) [**ICML 2023**]

Wuhan University

> ### New Perspective Based

* [**Continual Learning Should Move Beyond Incremental Classification**](https://arxiv.org/pdf/2502.11927v1) [**Arxiv 2025.02**]

Hessian Center for AI

* [**Continual learning in the brain**](https://escholarship.org/content/qt88p0463m/qt88p0463m.pdf) [**Arxiv 2025**]

UNIVERSITY OF CALIFORNIA, IRVINE

* [**GRAPHMOE: Amplifying Cognitive Depth of Mixture-of-Experts Network via Introducing Self-Rethinking Mechanism**](https://arxiv.org/pdf/2501.07890) [**Arxiv 2025.01**]

Institute for Advanced Algorithms Research,  Institute of Computing Technology CAS, Harbin Institute of Technology

* [**ZeroFlow: Overcoming Catastrophic Forgetting is Easier than You Think**](https://arxiv.org/pdf/2501.01045) [**Arxiv 2025.01**]

Tsinghua University, DAMO Academy

* [**Continual Learning Using a Kernel-Based Method Over Foundation Models**](https://arxiv.org/pdf/2412.15571) [**AAAI 2025**]

University of Illinois Chicago, Intel Labs

* [**Memory-efficient Continual Learning with Neural Collapse Contrastive**](https://arxiv.org/pdf/2412.02865) [**WACV 2025**]

Universite d’Orl ´ eans, ETIS - CY Cergy Paris University

* [**Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual

Learning**](https://arxiv.org/pdf/2411.15469) [**Arxiv 2024.11**]

Xidian University, Northwestern Polytechnical University

* [**Model Sensitivity Aware Continual Learning**](https://openreview.net/pdf?id=B5vQ7IQW7d) [**NeurIPS 2024**]

University of Maryland College Park

* [**Random Representations Outperform Online Continually Learned Representations**](https://arxiv.org/pdf/2402.08823) [**NeurIPS 2024**]

University of Oxford, IIIT Hyderabad, Apple

* [**Sparse Orthogonal Parameters Tuning for Continual Learning**](https://arxiv.org/pdf/2411.02813) [**Arxiv 2024.11**]

Peking University, Shenzhen

* [**Computationally Budgeted Continual Learning: What Does Matter?**](https://openaccess.thecvf.com/content/CVPR2023/papers/Prabhu_Computationally_Budgeted_Continual_Learning_What_Does_Matter_CVPR_2023_paper.pdf) [**CoLLAs 2024**]

University of T ¨ ubingen, Helmholtz Munich, Google DeepMind, TU Munich

* [**Reflecting on the State of Rehearsal-free Continual Learning with Pretrained Models**](https://arxiv.org/pdf/2406.09384) [**CVPR 2023**]

University of Oxford, KAUST, Meta AI

> ### Application

> #### Embodied AI

* [**iManip: Skill-Incremental Learning for Robotic Manipulation**](https://arxiv.org/pdf/2503.07087) [**Arxiv 2025.03**]

Sun Yat-sen University

* [**Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation**](https://arxiv.org/pdf/2410.22658) [**NeurIPS 2024**]

Sungkyunkwan University, Carnegie Mellon University

* [**Incremental Learning for Robot Shared Autonomy**](https://arxiv.org/pdf/2410.06315) [**Arxiv 2024.10**]

Robotics Institute, Carnegie Mellon University

* [**Task-unaware Lifelong Robot Learning with Retrieval-based Weighted Local Adaptation**](https://arxiv.org/pdf/2410.02995) [**Arxiv 2024.10**]

TU Delft, Booking.com, UCSD

* [**Vision-Language Navigation with Continual Learning**](https://arxiv.org/pdf/2409.02561) [**Arxiv 2024.09**]

Institute of Automation, Chinese Academy of Science

* [**Continual Vision-and-Language Navigation**](https://arxiv.org/pdf/2403.15049) [**Arxiv 2024.03**]

Seoul National University

* [**Online Continual Learning For Interactive Instruction Following Agents**](https://arxiv.org/pdf/2403.07548) [**ICLR 2024**]

Yonsei University, Seoul National University

* [**VOYAGER: An Open-Ended Embodied Agent with Large Language Models**](https://arxiv.org/pdf/2403.07548) [**NeurIPSW 2023**]

NVIDIA, Caltech, UT Austin, Stanford, UW Madison

* [**CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents**](https://proceedings.mlr.press/v199/powers22b/powers22b.pdf) [**CoLLAs 2022**]

Carnegie Mellon University, Georgia Institute of Technology, Allen Institute for AI

> #### RL

* [**Stable Continual Reinforcement Learning via Diffusion-based Trajectory Replay**](https://arxiv.org/pdf/2411.10809) [**ICLRW 2024**]

Nanjing University

* [**Evaluations of the Gap between Supervised and Reinforcement Lifelong Learning on Robotic Manipulation Tasks**](https://proceedings.mlr.press/v164/yang22a/yang22a.pdf) [**CoRL 2022**]

Tsinghua University

* [**Towards continual reinforcement learning: A review and perspectives**](https://www.jair.org/index.php/jair/article/view/13673/26878) [**JAIR 2022**]

McGill University, Universit´e de Montr´eal, IBM Research, DeepMind

* [**Lifelong Learning with a Changing Action Set**](https://aaai.org/ojs/index.php/AAAI/article/view/5739/5595) [**AAAI 2020**]

University of Massachusetts Amherst, Adobe Research

* [**Continual Reinforcement Learning in 3D Non-stationary Environments**](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w15/Lomonaco_Continual_Reinforcement_Learning_in_3D_Non-Stationary_Environments_CVPRW_2020_paper.pdf) [**CVPRW 2020**]

University of Bologna, University of Michigan, Purdue University

* [**Continual Reinforcement Learning in 3D Non-stationary Environments**](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w15/Lomonaco_Continual_Reinforcement_Learning_in_3D_Non-Stationary_Environments_CVPRW_2020_paper.pdf) [**CVPRW 2020**]

University of Bologna, University of Michigan, Purdue University

* [**Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer**](https://arxiv.org/pdf/1906.04452) [**ICMLW 2019**]

INRIA, AI Lab, Softbank Robotics Europe , Theresis Lab, Thales

> #### LLM

* [**Recurrent Knowledge Identification and Fusion for Language Model Continual Learning**](https://arxiv.org/pdf/2502.17510) [**Arxiv 2025.02**]

The Hong Kong Polytechnic University, Tsinghua University, Peking University, Huawei Hong Kong Research Center, University of Illinois at Chicago

* [**From RAG to Memory: Non-Parametric Continual Learning for Large Language Models**](https://arxiv.org/abs/2502.14802) [**Arxiv 2025.02**]

The Ohio State University, University of Illinois Urbana-Champaign

* [**Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion**](https://arxiv.org/abs/2502.12598) [**Arxiv 2025.02**]

Bosch Center for Artificial Intelligence

* [**DATA: Decomposed Attention-based Task Adaptation for Rehearsal-Free Continual Learning**](https://arxiv.org/pdf/2502.11482) [**Arxiv 2025.02**]

Institute of Automation, Chinese Academy of Sciences

* [**Mitigating Visual Knowledge Forgetting in MLLM Instruction-tuning via Modality-decoupled Gradient Descent**](https://arxiv.org/pdf/2502.11740) [**Arxiv 2025.02**]

UC San Diego

* [**Continual LLaVA: Continual Instruction Tuning in Large Vision-Language Models**](https://arxiv.org/pdf/2411.02564) [**Arxiv 2024.11**]

Mohamed bin Zayed University of Artificial Intelligence, MEGVII Technolog, Fudan University, Sun Yat-sen University

* [**LLMs Can Evolve Continually on Modality for X-Modal Reasoning**](https://arxiv.org/pdf/2410.20178) [**Arxiv 2024.10**]

Dalian University of Technology, Huawei Noah’s Ark Lab, Tsinghua University, HKUST

* [**LLaCA: Multimodal Large Language Continual Assistant**](https://arxiv.org/pdf/2410.10868) [**Arxiv 2024.10**]

East China Normal University, Xiamen University, Tencent YouTu Lab

* [**Is Parameter Collision Hindering Continual Learning in LLMs?**](https://arxiv.org/pdf/2410.10179) [**Arxiv 2024.10**]

Peking University, DAMO Academy

* [**CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language Model**](https://arxiv.org/abs/2403.08350) [**Arxiv 2024.10**]

UESTC, Tongji University

* [**ModalPrompt:Dual-Modality Guided Prompt for Continual Learning of Large Multimodal Models**](https://arxiv.org/pdf/2410.05849) [**Arxiv 2024.10**]

Institute of Automation, CAS

* [**Learning Attentional Mixture of LoRAs for Language Model Continual Learning**](https://arxiv.org/pdf/2406.16554)[**Arxiv 2024.09**]

Nankai University

* [**LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training**](https://arxiv.org/pdf/2409.19611)[**Arxiv 2024.06**]

Soochow University,  Shanghai AI Laboratory,  Shanghai Jiao Tong University,  Fudan University, CUHK

* [**LLAMA PRO: Progressive LLaMA with Block Expansion**](https://arxiv.org/pdf/2401.02415)[**Arxiv 2024.05**]

The University of Hong Kong, Tencent PCG, Shanghai Jiao Tong University , Beijing Language and Culture University

* [**COPR: Continual Learning Human Preference through Optimal Policy Regularization**](https://arxiv.org/pdf/2310.15694)[**Arxiv 2024.05**]

Harbin Institute of Technology (Shenzhen), Peng Cheng Laboratory, KCL

* [**CPPO: Continual Learning for Reinforcement Learning with Human Feedback**](https://openreview.net/pdf?id=86zAUE80pP) [**ICLR 2024**]

Harbin Institute of Technology (Shenzhen), Peng Cheng Laboratory, KCL

* [**Empowering Large Language Model for Continual Video Question Answering with Collaborative Prompting**](https://arxiv.org/pdf/2410.00771) [**EMNLP 2024**]

Nanyang Technological University

* [**Trace: A comprehensive benchmark for continual learning in large language models**](https://arxiv.org/pdf/2310.06762) [**Arxiv 2023.10**]

Fudan University, UCSB, Shanghai AI Laboratory

* [**ConPET: Continual Parameter-Efficient Tuning for Large Language Models**](https://arxiv.org/abs/2309.14763) [**Arxiv 2023.09**]

Tsinghua University, Tencent

* [**Continual Pre-Training of Large Language Models: How to (re)warm your model?**](https://openreview.net/pdf?id=pg7PUJe0Tl) [**ICML 2023**]

Universite de Montr ´ eal

* [**Lifelong Language Pretraining with Distribution-Specialized Experts**](https://proceedings.mlr.press/v202/chen23aq/chen23aq.pdf) [**ICML 2023**]

The University of Texas at Austin, Google

* [**Exploring Continual Learning for Code Generation Models**](https://arxiv.org/pdf/2307.02435) [**ACL 2023**]

University of North Carolina, AWS AI Labs, Amazon Alexa AI

* [**Drinking from a firehose: Continual learning with web-scale natural language**](https://arxiv.org/pdf/2007.09335) [**TPAMI 2023**]

University of Southern California, Intel Labs

* [**CITB: A Benchmark for Continual Instruction Tuning**](https://arxiv.org/abs/2310.14510) [**EMNLP 2023**]

University of Technology Sydney, University of Liverpool, University of Wollongong

* [**TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models**](https://arxiv.org/pdf/2204.14211) [**EMNLP 2022**]

KAIST, LG AI Research, Korea University

* [**Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora**](https://arxiv.org/pdf/2110.08534) [**NAACL 2022**]

University of Southern California, AWS AI Labs

* [**Towards continual knowledge learning of language models**](https://arxiv.org/pdf/2110.03215) [**ICLR 2022**]

KAIST, LG AI Research

* [**ConTinTin: Continual Learning from Task Instructions**](https://arxiv.org/abs/2203.08512) [**ACL 2022**]

Temple University, Salesforce Research

* [**TimeLMs: Diachronic Language Models from Twitter**](https://arxiv.org/pdf/2202.03829) [**ACL 2022**]

University of Porto, Snap, Cardiff University

* [**Dynamic language models for continuously evolving content**](https://dl.acm.org/doi/pdf/10.1145/3447548.3467162) [**KDD 2021**]

Google Research

> #### Diffusion

* [**Reward Incremental Learning in Text-to-Image Generation**](https://arxiv.org/pdf/2411.17310) [**Arxiv 2024.11**]

CyberAgent, The University of Tokyo

* [**Incremental Image Generation with Diffusion Models by Label Embedding Initialization and Fusion**](https://dl.acm.org/doi/pdf/10.1145/3688859.3690084) [**ACMMM 2024**]

Nanjing University, Tencent AI Lab

* [**Assessing Open-world Forgetting in Generative Image Model Customization**](https://arxiv.org/pdf/2410.14159) [**Arxiv 2024.10**]

Computer Vision Center, Universitat Autonoma de Barcelona

* [**Low-Rank Continual Personalization of Diffusion Models**](https://arxiv.org/pdf/2410.04891) [**Arxiv 2024.10**]

Warsaw University of Technology

* [**Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters**](https://openaccess.thecvf.com/content/CVPR2024W/MMFM/papers/Smith_Continual_Diffusion_with_STAMINA_STack-And-Mask_INcremental_Adapters_CVPRW_2024_paper.pdf) [**CVPRW 2024**]

Samsung Research America, Georgia Institute of Technology

* [**Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA**](https://arxiv.org/pdf/2304.06027) [**TMLR 2024**]

Samsung Research America, Georgia Institute of Technology

* [**Continual Learning of Diffusion Models with Generative Distillation**](https://arxiv.org/pdf/2311.14028) [**CoLLAs 2024**]

Master in Computer Vision (Barcelona), Apple, KU Leuven

> #### DeepFake

* [**Conditioned Prompt-Optimization for Continual Deepfake Detection**](https://arxiv.org/pdf/2407.21554) [**ICPR 2024**]

University of Trento, Fondazione Bruno Kessler

* [**A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials**](https://openaccess.thecvf.com/content/WACV2023/papers/Li_A_Continual_Deepfake_Detection_Benchmark_Dataset_Methods_and_Essentials_WACV_2023_paper.pdf) [**WACV 2023**]

ETH Zurich, Singapore Management University, Xi’an Jiaotong University, Harbin Institute of Technology, KU Leuven

## Acknowledge 👨‍🏫

* Figure from this URL: [Lifelong learning? Part-time undergraduate provision is in crisis](https://world.edu/lifelong-learning-part-time-undergraduate-provision-crisis/).