Summary of Hyperinterval: Hypernetwork Approach to Training Weight Interval Regions in Continual Learning, by Patryk Krukowski et al.
HyperInterval: Hypernetwork approach to training weight interval regions in continual learning
by Patryk Krukowski, Anna Bielawska, Kamil Książek, Paweł Wawrzyński, Paweł Batorski, Przemysław Spurek
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper presents a new Continual Learning (CL) paradigm called Interval Continual Learning (InterContiNet) to control catastrophic forgetting, which relies on enforcing interval constraints on the neural network parameter space. To address the issue of high dimensionality in the weight space, the authors introduce their technique, which employs interval arithmetic within the embedding space and utilizes a hypernetwork to map these intervals to the target network parameter space. The model preserves the response of the target network for previous task embeddings and allows faster and more efficient training while maintaining the guarantee of not forgetting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Interval Continual Learning (InterContiNet) is a new way to help neural networks remember what they learned in the past when presented with new tasks. This helps prevent the problem known as catastrophic forgetting, where a network forgets previously learned information. The authors introduce their own method that uses something called interval arithmetic within an “embedding space” and a special kind of neural network called a hypernetwork to help the main network learn and remember. |
Keywords
» Artificial intelligence » Continual learning » Embedding space » Neural network
