A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning

• URL: http://arxiv.org/abs/2307.09218v3
• Date: Mon, 18 Nov 2024 13:26:41 GMT
• Title: A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning
• Authors: Zhenyi Wang, Enneng Yang, Li Shen, Heng Huang,
• Abstract summary: Forgetting refers to the loss or deterioration of previously acquired knowledge. Forgetting is a prevalent phenomenon observed in various other research domains within deep learning.
• Score: 58.107474025048866
• License:
• Abstract: Forgetting refers to the loss or deterioration of previously acquired knowledge. While 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 task, 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 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, we hope to encourage the development of novel strategies for mitigating, harnessing, or even embracing forgetting in real applications. A comprehensive list of papers about forgetting in various research fields is available at \url{https://github.com/EnnengYang/Awesome-Forgetting-in-Deep-Learning}.

Related papers

• Model Inversion Attacks: A Survey of Approaches and Countermeasures [59.986922963781]
  Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training. Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs. This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-15T08:09:28Z)
• Continual Learning in Medical Imaging: A Survey and Practical Analysis [0.7794090852114541]
  Continual Learning offers promise in enabling the sequential acquisition of new knowledge without forgetting previous learnings in neural networks. We review the recent literature on continual learning in the medical domain, highlight recent trends, and point out the practical issues. We critically discuss the current state of continual learning in medical imaging, including identifying open problems and outlining promising future directions.
  arXiv  Detail & Related papers  (2024-05-22T09:48:04Z)
• Machine Unlearning: A Survey [56.79152190680552]
  A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
  arXiv  Detail & Related papers  (2023-06-06T10:18:36Z)
• On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions [36.733628606028184]
  We present an extensive survey on the topic of knowledge editing (augmentation/removal) in Federated Learning. We introduce an integrated paradigm, referred to as Federated Editable Learning (FEL), by reevaluating the entire lifecycle of FL.
  arXiv  Detail & Related papers  (2023-06-02T10:42:47Z)
• Knowledge-augmented Deep Learning and Its Applications: A Survey [60.221292040710885]
  knowledge-augmented deep learning (KADL) aims to identify domain knowledge and integrate it into deep models for data-efficient, generalizable, and interpretable deep learning. This survey subsumes existing works and offers a bird's-eye view of research in the general area of knowledge-augmented deep learning.
  arXiv  Detail & Related papers  (2022-11-30T03:44:15Z)
• An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey [0.0]
  We propose to survey these research works through a new taxonomy based on information theory. We computationally revisit the notions of surprise, novelty and skill learning. Our analysis suggests that novelty and surprise can assist the building of a hierarchy of transferable skills.
  arXiv  Detail & Related papers  (2022-09-19T09:47:43Z)
• Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey [53.73359052511171]
  Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. We present a framework for curriculum learning (CL) in RL, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals.
  arXiv  Detail & Related papers  (2020-03-10T20:41:24Z)
• Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills [155.11646755470582]
  'Explore, Discover and Learn' (EDL) is an alternative approach to information-theoretic skill discovery. We show that EDL offers significant advantages, such as overcoming the coverage problem, reducing the dependence of learned skills on the initial state, and allowing the user to define a prior over which behaviors should be learned.
  arXiv  Detail & Related papers  (2020-02-10T10:49:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.