Summary of Balanced Gradient Sample Retrieval For Enhanced Knowledge Retention in Proxy-based Continual Learning, by Hongye Xu et al.
Balanced Gradient Sample Retrieval for Enhanced Knowledge Retention in Proxy-based Continual Learning
by Hongye Xu, Jan Wasilewski, Bartosz Krawczyk
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 novel sample retrieval strategy from the memory buffer leverages both gradient-conflicting and gradient-aligned samples to effectively retain knowledge about past tasks within a supervised contrastive learning framework. The approach balances gradient correction from conflicting samples with alignment reinforcement from aligned ones, increasing the diversity among retrieved instances and achieving superior alignment in parameter space. This leads to significantly enhanced knowledge retention and mitigated proxy drift, outperforming methods relying solely on one sample type or random retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists try to solve a problem called catastrophic forgetting in deep neural networks. They find that when these networks learn new things, they often forget old information. To fix this, they create a new way of getting old data from memory that helps keep the network’s knowledge about past tasks. This approach uses two types of samples: ones that help correct errors and ones that make sure the network is consistent with its previous learning. By combining these two approaches, the scientists show that their method can retain more information about past tasks while still being good at learning new things. |
Keywords
» Artificial intelligence » Alignment » Supervised
