Summary of On the Causal Sufficiency and Necessity Of Multi-modal Representation Learning, by Jingyao Wang et al.
On the Causal Sufficiency and Necessity of Multi-Modal Representation Learning
by Jingyao Wang, Siyu Zhao, Wenwen Qiang, Jiangmeng Li, Fuchun Sun, Hui Xiong
First submitted to arxiv on: 19 Jul 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 A novel approach to Multi-Modal Learning (MML) is proposed, which focuses on learning effective representations across modalities for accurate predictions. The existing methods prioritize modality consistency and specificity, but this may lead to representations containing insufficient and unnecessary information. To address this, the authors introduce a concept called Causal Complete Cause (C^3), which quantifies the probability of representations being causally sufficient and necessary. A twin network is designed to estimate C^3 risk, consisting of real-world and hypothetical-world branches. Theoretical analyses confirm its reliability, and experiments demonstrate the effectiveness of C^3 Regularization, a plug-and-play method that enforces causal completeness of learned representations by minimizing C^3 risk. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multi-Modal Learning is trying to make computers understand things from different sources, like pictures and words. Right now, most methods are good at making sure these sources match up well, but they might also learn unnecessary information. The authors propose a new way to think about this problem by introducing the concept of Causal Complete Cause (C^3). They show that it’s possible to use special networks to estimate how well their method is working and provide a simple solution called C^3 Regularization. This method can be used with any existing learning algorithm, and tests show it improves results. |
Keywords
» Artificial intelligence » Multi modal » Probability » Regularization
