Summary of Imex-reg: Implicit-explicit Regularization in the Function Space For Continual Learning, by Prashant Bhat et al.
IMEX-Reg: Implicit-Explicit Regularization in the Function Space for Continual Learning
by Prashant Bhat, Bharath Renjith, Elahe Arani, Bahram Zonooz
First submitted to arxiv on: 28 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 Continual learning (CL) for deep neural networks faces a long-standing challenge: catastrophic forgetting of previously acquired knowledge. Rehearsal-based approaches have mitigated this issue but suffer from overfitting and prior information loss, hindering generalization under low-buffer regimes. Inspired by human learning, we propose IMEX-Reg to improve experience rehearsal in CL using contrastive representation learning (CRL) and consistency regularization. Our approach improves generalization performance, outperforming rehearsal-based methods in various scenarios. We also provide theoretical insights supporting our design decisions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how deep neural networks can keep learning new things without forgetting what they already know. Right now, this is a big challenge for these networks. Some ways to solve this problem exist, but they have some drawbacks. This paper proposes a new approach that combines two techniques: contrastive representation learning and consistency regularization. The results show that our approach works well and can even handle noisy or corrupted data. We also explain why we designed it in a certain way. |
Keywords
» Artificial intelligence » Continual learning » Generalization » Overfitting » Regularization » Representation learning
