Summary of A Theoretical Characterization Of Optimal Data Augmentations in Self-supervised Learning, by Shlomo Libo Feigin et al.
A Theoretical Characterization of Optimal Data Augmentations in Self-Supervised Learning
by Shlomo Libo Feigin, Maximilian Fleissner, Debarghya Ghoshdastidar
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 challenges the conventional understanding of self-supervised learning (SSL) and its reliance on data augmentations. While it’s commonly thought that SSL requires diverse augmentations that resemble the data to work well, empirical evidence suggests otherwise. The authors use kernel theory to derive analytical expressions for constructing optimal data augmentations that achieve desired target representations after pretraining. They analyze two popular non-contrastive losses, VICReg and Barlow Twins, and provide an algorithm for constructing such augmentations. The findings show that augmentations do not need to be similar to the data or diverse to learn useful representations, and that the architecture has a significant impact on the optimal augmentations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how self-supervised learning (SSL) works with data augmentations. Right now, people think that SSL needs lots of different ways to change the data to make it work well. But some experiments have shown this isn’t true. The authors use math to figure out what makes good data augmentations and then test two popular methods called VICReg and Barlow Twins. They find that you don’t need to change the data in lots of different ways, and that the way your computer is set up also matters. |
Keywords
» Artificial intelligence » Pretraining » Self supervised
