Abstract
This chapter delves into the dynamics of continual learning, which is the process of incrementally learning from a nonstationary 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 chapter reviews the insights that this field has generated.
| Original language | English |
|---|---|
| Title of host publication | Learning and Memory |
| Subtitle of host publication | A Comprehensive Reference |
| Editors | J Wixted |
| Publisher | Academic Press |
| Pages | V1:153-V1:168 |
| Edition | Third |
| ISBN (Electronic) | 9780443157547 |
| ISBN (Print) | 9780443157554 |
| DOIs | |
| Publication status | Published - 2025 |
| Externally published | Yes |
Keywords
- Catastrophic forgetting
- Cognitive science
- Context-dependent processing
- Continual learning
- Deep learning
- Deep neural networks
- Functional regularization
- Incremental learning
- Lifelong learning
- Optimization-based approaches
- Parameter regularization
- Replay
- Template-based classification
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