Summary of Contrasting with Symile: Simple Model-agnostic Representation Learning For Unlimited Modalities, by Adriel Saporta et al.
Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities
by Adriel Saporta, Aahlad Puli, Mark Goldstein, Rajesh Ranganath
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 The paper presents a new contrastive learning approach called Symile, which captures higher-order information between any number of modalities. Unlike traditional methods like CLIP, which pair two modalities and fail to capture joint information, Symile provides a flexible objective for learning modality-specific representations. The authors derive a lower bound on total correlation to develop the Symile objective and show that it forms a sufficient statistic for predicting remaining modalities. Symile outperforms pairwise CLIP on cross-modal classification and retrieval tasks, even with missing modalities in the data. The approach is demonstrated on several experiments, including a multilingual dataset of image, text, and audio samples, as well as a clinical dataset of chest X-rays, electrocardiograms, and laboratory measurements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces Symile, a new way to learn representations from multiple types of data. Usually, we only pair two types of data together, but this doesn’t work well when we have many different kinds of data. Symile is a simple approach that captures the relationships between any number of different types of data. This helps us learn better representations that can be used for tasks like image and text classification. The authors show that Symile works better than previous approaches on several experiments, including one with a large dataset of images, text, and audio samples. |
Keywords
» Artificial intelligence » Classification » Text classification
