Summary of Ds-al: a Dual-stream Analytic Learning For Exemplar-free Class-incremental Learning, by Huiping Zhuang et al.
DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning
by Huiping Zhuang, Run He, Kai Tong, Ziqian Zeng, Cen Chen, Zhiping Lin
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The proposed Dual-Stream Analytic Learning (DS-AL) approach addresses the exemplar-free class-incremental learning (CIL) problem, which is prone to catastrophic forgetting. The DS-AL consists of a main stream that redefines the CIL problem as a Concatenated Recursive Least Squares (C-RLS) task and a compensation stream governed by a Dual-Activation Compensation (DAC) module. This approach delivers performance comparable with or better than replay-based methods on various datasets, including CIFAR-100, ImageNet-100, and ImageNet-Full. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to learn without forgetting, using an “analytic” approach that’s different from other methods. It’s like a special trick to help the model remember what it learned before, so it can keep learning even when it gets new data. The authors tested their method on some big datasets and showed that it works just as well as more complicated approaches. |
