Summary of Ldreg: Local Dimensionality Regularized Self-supervised Learning, by Hanxun Huang et al.
LDReg: Local Dimensionality Regularized Self-Supervised Learning
by Hanxun Huang, Ricardo J. G. B. Campello, Sarah Monazam Erfani, Xingjun Ma, Michael E. Houle, James Bailey
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 This paper investigates the phenomenon of “dimensional collapse” in self-supervised learning (SSL), where representations learned via SSL are limited to extremely low dimensional spaces, leading to poor performance on downstream tasks. The authors show that while representations may span high-dimensional spaces globally, they can still collapse locally. To address this issue, the authors propose a method called Local Dimensionality Regularization (LDReg), which uses the Fisher-Rao metric to optimize local distance distributions and increase the intrinsic dimensionality of representations. Experiments demonstrate that LDReg improves representation quality and regularizes dimensionality at both local and global levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how machines learn from themselves without being told what to do. Sometimes, this learning process gets stuck in a low-dimensional space, which means it can’t capture all the information needed for future tasks. The authors found that even though the representations might look good globally, they’re still limited locally. To fix this, they created a new method called LDReg, which helps machines learn more complex and useful representations by optimizing how they compare to each other. This leads to better performance on future tasks. |
Keywords
* Artificial intelligence * Regularization * Self supervised
