Summary of Rethinking Generalizability and Discriminability Of Self-supervised Learning From Evolutionary Game Theory Perspective, by Jiangmeng Li et al.
Rethinking Generalizability and Discriminability of Self-Supervised Learning from Evolutionary Game Theory Perspective
by Jiangmeng Li, Zehua Zang, Qirui Ji, Chuxiong Sun, Wenwen Qiang, Junge Zhang, Changwen Zheng, Fuchun Sun, Hui Xiong
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 abstract discusses the limitations of self-supervised learning methods, which typically excel in either generalizability or discriminability but not both simultaneously. The authors propose a novel approach that leverages reinforcement learning to jointly optimize for generalizability and discriminability, building upon evolutionary game theory (EGT). This method is designed to sequentially improve representation quality during pre-training, achieving state-of-the-art performance on various benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores the limitations of self-supervised learning methods, which often prioritize either generalizability or discriminability. The authors propose a new approach that combines reinforcement learning and evolutionary game theory (EGT) to optimize for both qualities simultaneously. This method aims to improve representation quality during pre-training, achieving better performance on various benchmarks. |
Keywords
» Artificial intelligence » Reinforcement learning » Self supervised
