Summary of Adaptive Adapter Routing For Long-tailed Class-incremental Learning, by Zhi-hong Qi et al.
Adaptive Adapter Routing for Long-Tailed Class-Incremental Learning
by Zhi-Hong Qi, Da-Wei Zhou, Yiran Yao, Han-Jia Ye, De-Chuan Zhan
First submitted to arxiv on: 11 Sep 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 AdaPtive Adapter RouTing (APART) model addresses the challenge of long-tailed class-incremental learning (LTCIL) by leveraging pre-trained models and introducing exemplar-free solutions. APART uses adapters with frozen pre-trained weights to learn from new data without forgetting, maintaining a pool of adapters for selection during sequential updates. This approach also includes an auxiliary adapter pool for generalization on minority classes. The model’s effectiveness is validated through extensive benchmark experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary APART helps machines learn from big datasets that are not equally balanced, like how many people review online stores. It uses old knowledge to teach new things without forgetting what it already knows. This helps with a problem called long-tailed class-incremental learning (LTCIL). APART is a special way of updating models so they can keep learning and remember important details. |
Keywords
» Artificial intelligence » Generalization
