Summary of An Information Criterion For Controlled Disentanglement Of Multimodal Data, by Chenyu Wang et al.
An Information Criterion for Controlled Disentanglement of Multimodal Data
by Chenyu Wang, Sharut Gupta, Xinyi Zhang, Sana Tonekaboni, Stefanie Jegelka, Tommi Jaakkola, Caroline Uhler
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
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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 proposed Disentangled Self-Supervised Learning (DisentangledSSL) approach enables multimodal representation learning by disentangling modality-specific information from shared information across modalities. This allows for improved interpretability, robustness, and the generation of counterfactual outcomes in downstream tasks such as vision-language data prediction and molecule-phenotype retrieval. The method learns shared and modality-specific features on multiple synthetic and real-world datasets, outperforming baselines in various tasks. The optimality of each disentangled representation is analyzed, including scenarios where the Minimum Necessary Information (MNI) point is not attainable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DisentangledSSL helps computers understand different types of information, like images and words. It does this by separating special features that belong to one type of information from shared features that appear in multiple types. This makes it easier for computers to generate new information, like counterfactual outcomes. The method is tested on many datasets and performs well in tasks like predicting what an image says or finding related molecules. |
Keywords
» Artificial intelligence » Representation learning » Self supervised
