Summary of Multimodal Fusion Balancing Through Game-theoretic Regularization, by Konstantinos Kontras et al.
Multimodal Fusion Balancing Through Game-Theoretic Regularization
by Konstantinos Kontras, Thomas Strypsteen, Christos Chatzichristos, Paul Pu Liang, Matthew Blaschko, Maarten De Vos
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Computer Science and Game Theory (cs.GT); Multimedia (cs.MM)
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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 Multimodal Competition Regularizer (MCR) aims to overcome modality competition in multimodal training by introducing game-theoretic principles. The MCR loss component, inspired by mutual information decomposition, prevents the adverse effects of competition, enabling automatic balancing of MI terms. This paper’s key contributions include refining lower and upper bounds for each MI term, suggesting latent space permutations for conditional MI estimation, and demonstrating significant performance gains on both synthetic and large real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal learning can help us better understand information by finding connections between different data sources. However, current systems don’t always use multiple types of data effectively. To fix this, the authors propose a new way to balance the training process called the Multimodal Competition Regularizer (MCR). The MCR helps prevent some data sources from getting left behind and ensures that learning from new data consistently improves performance. |
Keywords
» Artificial intelligence » Latent space
