Summary of Beyond Pairwise Correlations: Higher-order Redundancies in Self-supervised Representation Learning, by David Zollikofer et al.
Beyond Pairwise Correlations: Higher-Order Redundancies in Self-Supervised Representation Learning
by David Zollikofer, Béni Egressy, Frederik Benzing, Matthias Otth, Roger Wattenhofer
First submitted to arxiv on: 2 Dec 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 The abstract discusses self-supervised learning (SSL) approaches for representation learning, specifically focusing on reducing redundancy in the feature embedding space. The current methods consider pairwise correlations between features, but this paper introduces new metrics to capture higher-order dependencies and proposes Self Supervised Learning with Predictability Minimization (SSLPM) as a method to reduce redundancy. SSLPM combines an encoder network with a predictor engaged in a competitive game of reducing and exploiting dependencies respectively. This approach is shown to be competitive with state-of-the-art methods, and the best performing SSL methods are found to have low embedding space redundancy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Self-supervised learning (SSL) helps computers learn without human guidance. Researchers have discovered that by removing “redundancy” from the information learned, they can improve how well the computer represents real-world things like images and sounds. But current methods only look at simple relationships between features, not more complex ones. This paper introduces new ways to measure redundancy and proposes a new method called Self Supervised Learning with Predictability Minimization (SSLPM) that reduces redundancy. The results show that this method is just as good as others, and the best methods are those that implicitly reduce redundancy. |
Keywords
» Artificial intelligence » Embedding space » Encoder » Representation learning » Self supervised
