Summary of Contrasting Multiple Representations with the Multi-marginal Matching Gap, by Zoe Piran et al.
Contrasting Multiple Representations with the Multi-Marginal Matching Gap
by Zoe Piran, Michal Klein, James Thornton, Marco Cuturi
First submitted to arxiv on: 29 May 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 Medium Difficulty summary: This paper proposes a novel loss function called the multi-marginal matching gap (M3G) that tackles learning meaningful representations of complex objects across multiple views or modalities. The existing methods extend paired-view losses to accommodate more than two views, either by creating multiple pairs of views or using reduced embeddings. In contrast, M3G draws from multi-marginal optimal transport theory to simultaneously incorporate all views. Given a batch of points, each viewed as a k-tuple, the loss contrasts the cost of matching these ground-truth k-tuples with the MM-OT polymatching cost. The authors demonstrate improved performance over multiview extensions of pairwise losses for both self-supervised and multimodal tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper is about creating a new way to learn patterns in images or data that can be seen from different angles or using different sensors. Right now, computers are not very good at this task because they only look at two views at a time. The authors propose a new method called M3G that can handle more than two views and does better than the current methods. They tested their method on some tasks and it performed well. |
Keywords
» Artificial intelligence » Loss function » Self supervised
