Summary of Occam’s Razor For Self Supervised Learning: What Is Sufficient to Learn Good Representations?, by Mark Ibrahim et al.
Occam’s Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations?
by Mark Ibrahim, David Klindt, Randall Balestriero
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a study that challenges the conventional wisdom on Self-Supervised Learning (SSL) by showing that additional designs introduced by SSL do not contribute to the quality of learned representations. The authors analyze pre-training datasets with up to a few hundred thousand samples and find that these design choices do not improve representation quality. This finding has implications for both theoretical studies and practitioners, as it simplifies the deployment of SSL methods in small and medium-scale settings. The study contributes to the understanding of SSL by answering a long-standing question about the sensitivity of training settings and hyper-parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that extra designs in Self-Supervised Learning (SSL) don’t make a difference in how well it works. The researchers looked at big datasets with millions of examples, but also smaller ones with only thousands or tens of thousands. They found that the extra designs didn’t help improve the quality of what the computer learned. This is important because it makes it easier for people to use SSL methods without having to adjust many settings. |
Keywords
* Artificial intelligence * Self supervised
