Summary of Infonce: Identifying the Gap Between Theory and Practice, by Evgenia Rusak et al.
InfoNCE: Identifying the Gap Between Theory and Practice
by Evgenia Rusak, Patrik Reizinger, Attila Juhos, Oliver Bringmann, Roland S. Zimmermann, Wieland Brendel
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 A novel generalized contrastive learning model, AnInfoNCE, is introduced to uncover latent factors in anisotropic settings, deviating from previous InfoNCE-based theories. These theories assume identical or no variation within positive pairs, whereas practical implementations often involve strong augmentations like pixel-level cropping, resulting in a continuum of variability across factors. AnInfoNCE proves identifiable and shows improved recovery of previously collapsed information in CIFAR10 and ImageNet, albeit at the expense of downstream accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Contrastive learning is a way to train AI models using positive pairs of images that are similar, and negative pairs that are very different. Researchers have shown that this approach can help learn important features about objects and scenes. However, previous theories assumed that when we generate these positive pairs, all the underlying factors that make them similar either change a lot or not at all. But in reality, we often use strong augmentations like cropping just a few pixels to create these positive pairs. This means that all the underlying factors can change, and some might change more than others. The new model, AnInfoNCE, takes this into account and can better uncover the important features of objects and scenes. |
