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About this paper
Zhenyi Wang, Enneng Yang, Li Shen, Heng Huang
This survey redefines forgetting in deep learning, extending its study beyond continual learning to encompass a wide array of domains. It systematically categorizes forgetting into harmful and beneficial forms, providing a unified framework and identifying future research directions for understanding and harnessing this phenomenon.
Key insights
- Forgetting is a fundamental and pervasive phenomenon across diverse deep learning domains, extending far beyond the traditional scope of continual learning.
- The nature of forgetting is dual, encompassing both 'harmful' aspects (loss of valuable knowledge) and 'beneficial' aspects (e.g., mitigating overfitting, debiasing, enabling privacy-preserving machine unlearning).
- Understanding and categorizing forgetting across different fields reveals common underlying mechanisms and suggests opportunities for cross-disciplinary solutions and theoretical advancements.
Summary by alphaXiv.
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