Summary of Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning, by Jihai Zhang et al.
Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning
by Jihai Zhang, Xiang Lan, Xiaoye Qu, Yu Cheng, Mengling Feng, Bryan Hooi
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Self-Supervised Contrastive Learning approach has been successful in generating high-quality representations from unlabeled data. However, it faces a significant challenge – feature suppression, which occurs when the trained model captures only a limited portion of the input data’s information while overlooking other valuable content. This issue leads to indistinguishable representations for visually similar but semantically different inputs, negatively impacting downstream task performance, particularly those requiring rigorous semantic comprehension. To address this, the authors propose a novel Multistage Contrastive Learning (MCL) framework that progressively learns previously unlearned features through feature-aware negative sampling at each stage and preserves well-learned features by cross-stage representation integration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to improve contrastive learning called Multistage Contrastive Learning. It helps machines learn from pictures without needing labels. This is important because it can help make computers better at understanding what’s in the pictures. The authors tested their idea and found that it works really well, even when used with different types of computer models. |
Keywords
* Artificial intelligence * Self supervised
