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A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. TPAMI, 2024.
| Date | Stars |
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| 2026-07-31 | 366 |
| 2026-08-06 | 366 |
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# Awesome-Forgetting-in-Deep-Learning
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<img src="https://img.shields.io/badge/Contributions-Welcome-278ea5" alt=""/>
A comprehensive list of papers about **'[A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning](https://arxiv.org/abs/2307.09218)'**.
## Abstract
> Forgetting refers to the loss or deterioration of previously acquired information or knowledge. While the existing surveys on forgetting have primarily focused on continual learning, forgetting is a prevalent phenomenon observed in various other research domains within deep learning. Forgetting manifests in research fields such as generative models due to generator shifts, and federated learning due to heterogeneous data distributions across clients. Addressing forgetting encompasses several challenges, including balancing the retention of old task knowledge with fast learning of new tasks, managing task interference with conflicting goals, and preventing privacy leakage, etc. Moreover, most existing surveys on continual learning implicitly assume that forgetting is always harmful. In contrast, our survey argues that forgetting is a double-edged sword and can be beneficial and desirable in certain cases, such as privacy-preserving scenarios. By exploring forgetting in a broader context, we aim to present a more nuanced understanding of this phenomenon and highlight its potential advantages. Through this comprehensive survey, we aspire to uncover potential solutions by drawing upon ideas and approaches from various fields that have dealt with forgetting. By examining forgetting beyond its conventional boundaries, in future work, we hope to encourage the development of novel strategies for mitigating, harnessing, or even embracing forgetting in real applications.
## Citation
If you find our paper or this resource helpful, please consider citing:
```
@article{Forgetting_Survey_2024,
title={A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning},
author={Wang, Zhenyi and Yang, Enneng and Shen, Li and Huang, Heng},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2024},
publisher={IEEE}
}
```
Thanks!
******
## Framework
* [Harmful Forgetting](#harmful-forgetting)
+ [Forgetting in Continual Learning](#forgetting-in-continual-learning)
- [Survey and Book](#survey-and-book)
- [Task-aware CL](#task-aware-cl)
* [Memory-based Methods](#memory-based-methods)
* [Architecture-based Methods](#architecture-based-methods)
* [Regularization-based Methods](#regularization-based-methods)
* [Subspace-based Methods](#subspace-based-methods)
* [Bayesian Methods](#bayesian-methods)
- [Task-free CL](#task-free-cl)
- [Online CL](#online-cl)
- [Semi-supervised CL](#semi-supervised-cl)
- [Few-shot CL](#few-shot-cl)
- [Unsupervised CL](#unsupervised-cl)
- [Theoretical Analysis](#theoretical-analysis)
+ [Forgetting in Foundation Models](#forgetting-in-foundation-models)
- [Forgetting in Fine-Tuning Foundation Models](#forgetting-in-fine-tuning-foundation-models)
- [Forgetting in One-Epoch Pre-training](#forgetting-in-one-epoch-pre-training)
- [CL in Foundation Model](#cl-in-foundation-model)
+ [Forgetting in Domain Adaptation](#forgetting-in-domain-adaptation)
+ [Forgetting in Test-Time Adaptation](#forgetting-in-test-time-adaptation)
+ [Forgetting in Meta-Learning](#forgetting-in-meta-learning)
- [Incremental Few-Shot Learning](#incremental-few-shot-learning)
- [Continual Meta-Learning](#continual-meta-learning)
+ [Forgetting in Generative Models](#forgetting-in-generative-models)
- [GAN Training is a Continual Learning Problem](#gan-training-is-a-continual-learning-problem)
- [Lifelong Learning of Generative Models](#lifelong-learning-of-generative-models)
+ [Forgetting in Reinforcement Learning]Excerpt of 131,959 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1e06b152d7890dbc, llm:Repository is an 'Awesome' curated list and survey resources on forgetting in deep learning (README, topics: continual-learning, catastrophic-forgetting, machine-unlearning, federated-learning, generative-model, privacy attacks, meta-learning, reinforcement-learning).
matched fp:1e06b152d7890dbc, llm:Repository is an 'Awesome' curated list and survey resources on forgetting in deep learning (README, topics: continual-learning, catastrophic-forgetting, machine-unlearning, federated-learning, generative-model, privacy attacks, meta-learning, reinforcement-learning).