Continual Learning and Catastrophic Forgetting

Gido M. van de Ven, Nicholas Soures, Dhireesha Kudithipudi
3/8/2024
cs.LGcs.AIcs.CV

No confident code implementation found for this paper. 5 possible matches can be shown below.

Abstract

This book chapter delves into the dynamics of continual learning, which is the process of incrementally learning from a non-stationary stream of data. Although continual learning is a natural skill for the human brain, it is very challenging for artificial neural networks. An important reason is that, when learning something new, these networks tend to quickly and drastically forget what they had learned before, a phenomenon known as catastrophic forgetting. Especially in the last decade, continual learning has become an extensively studied topic in deep learning. This book chapter reviews the insights that this field has generated.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 5 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{ven2024continual,
  title  = {Continual Learning and Catastrophic Forgetting},
  author = {Gido M. van de Ven and Nicholas Soures and Dhireesha Kudithipudi},
  year   = {2024},
  eprint = {2403.05175},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.05175v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2403.05175.svg)](https://codeofpaper.com/paper/2403.05175)

Discussion