Summary of Embedding Geometries Of Contrastive Language-image Pre-training, by Jason Chuan-chih Chou et al.
Embedding Geometries of Contrastive Language-Image Pre-Training
by Jason Chuan-Chih Chou, Nahid Alam
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); 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 Medium Difficulty summary: This paper explores alternatives to the widely-used InfoNCE loss for contrastive pre-training in multimodal models. Specifically, the authors experiment with different geometries and softmax logits in language-image pre-training, and find that Euclidean geometry (EuCLIP) matches or exceeds the performance of CLIP. The results also show that EuCLIP supports hierarchical relationships at least as well as more complex hyperbolic alternatives. This work sheds light on the design choices underlying popular multimodal models like CLIP and may inform future developments in this area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: A new paper looks at how to improve a type of artificial intelligence called contrastive pre-training. Contrastive pre-training helps connect two different types of information, like pictures and words. The authors try out some new ways to do this pre-training and find that one approach, called Euclidean geometry, works just as well or even better than the original method. This could help make AI models that can understand and relate different types of information in a more powerful way. |
Keywords
» Artificial intelligence » Logits » Softmax
