Summary of Explicit Mutual Information Maximization For Self-supervised Learning, by Lele Chang and Peilin Liu and Qinghai Guo and Fei Wen
Explicit Mutual Information Maximization for Self-Supervised Learning
by Lele Chang, Peilin Liu, Qinghai Guo, Fei Wen
First submitted to arxiv on: 7 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper presents a novel approach to self-supervised learning (SSL) that maximizes mutual information (MI). By leveraging the invariance property of MI, the authors show that explicit MI maximization can be applied to SSL under a relaxed condition on the data distribution. This is achieved by deriving a loss function based on the MIM criterion using only second-order statistics. The effectiveness of the new approach is demonstrated through extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores self-supervised learning, which helps machines learn without labels. It shows how to make this work better by using mutual information maximization, an idea from information theory. The authors prove that even with limited information about the data, they can still use MI to improve SSL. They also create a new way to calculate loss based on this idea and test it out. |
Keywords
» Artificial intelligence » Loss function » Self supervised
