Summary of A Probabilistic Model Behind Self-supervised Learning, by Alice Bizeul et al.
A Probabilistic Model Behind Self-Supervised Learning
by Alice Bizeul, Bernhard Schölkopf, Carl Allen
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 A new generative latent variable model for self-supervised learning is proposed, which provides a unifying theoretical framework for several families of discriminative SSL methods. The model justifies connections drawn to mutual information and the use of a “projection head”. It outperforms existing methods on simple image benchmarks and narrows the gap between generative and discriminative representation learning in more complex settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Self-supervised learning is a way for machines to learn without being taught. This helps them understand what things are like, but not exactly how they look. A lot of different approaches have been tried, some doing well on certain tasks. But why do they work? We don’t really know yet. This paper tries to answer that question by creating a new way for machines to learn using an imaginary “latent variable model”. It shows that many ways of learning self-supervised are actually connected and do similar things. This helps us understand how these methods work and can even help them perform better on certain tasks. |
Keywords
* Artificial intelligence * Representation learning * Self supervised
