Summary of Probabilistic Contrastive Learning with Explicit Concentration on the Hypersphere, by Hongwei Bran Li et al.
Probabilistic Contrastive Learning with Explicit Concentration on the Hypersphere
by Hongwei Bran Li, Cheng Ouyang, Tamaz Amiranashvili, Matthew S. Rosen, Bjoern Menze, Juan Eugenio Iglesias
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 introduces a novel approach to self-supervised contrastive learning by incorporating uncertainty into the representation space. Building on the von Mises-Fisher distribution (vMF), it embeds representations within a spherical space, leveraging the concentration parameter, kappa, as an interpretable measure of uncertainty. This probabilistic interpretation of the embedding space enables model confidence calibration against varying levels of data corruption and characteristics. The approach demonstrates strong correlation with unforeseen data corruption at test time, facilitates failure analysis, and enhances existing out-of-distribution detection methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn from data without labels by considering uncertainty. Imagine you’re trying to understand how people dress based on pictures, but the pictures are blurry or have weird objects in them. The usual approach doesn’t work well in these situations because it assumes all the data is perfect. This paper shows that if we can understand when our model is unsure or confused, we can make it better at handling imperfect data. It uses a special math concept called von Mises-Fisher distribution to create a “cloud” of possible answers and then uses this cloud to figure out how confident the model should be in its predictions. |
Keywords
* Artificial intelligence * Embedding space * Self supervised
