Summary of Continual Learning and Catastrophic Forgetting, by Gido M. Van De Ven et al.
Continual Learning and Catastrophic Forgetting
by Gido M. van de Ven, Nicholas Soures, Dhireesha Kudithipudi
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Neurons and Cognition (q-bio.NC); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed book chapter explores the challenges of continual learning, a process where artificial neural networks learn from non-stationary data streams while maintaining previously acquired knowledge. The main issue is catastrophic forgetting, where networks quickly forget what they learned before when encountering new information. As a result, the field of deep learning has seen significant research on this topic in recent years. The chapter reviews the key insights generated by this body of work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Continual learning is like how our brains learn and remember new things while still keeping memories from the past. Artificial intelligence networks have trouble with this, forgetting what they learned before when they encounter new information. This is a big problem for AI, so scientists are working hard to solve it. The book chapter looks back at what we’ve learned about continual learning in deep learning over the last decade. |
Keywords
* Artificial intelligence * Continual learning * Deep learning
