Summary of Learning to Rebalance Multi-modal Optimization by Adaptively Masking Subnetworks, By Yang Yang et al.
Learning to Rebalance Multi-Modal Optimization by Adaptively Masking Subnetworks
by Yang Yang, Hongpeng Pan, Qing-Yuan Jiang, Yi Xu, Jinghui Tang
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 paper addresses the “modality imbalance” problem in multi-modal learning by developing a novel optimization strategy called Adaptively Mask Subnetworks Considering Modal Significance (AMSS). The approach aims to balance the optimization of each modality, taking into account their relative importance. By incorporating mutual information rates and non-uniform adaptive sampling, AMSS selects foreground subnetworks from each modality for parameter updates, thereby rebalancing multi-modal learning. The paper also presents a convergence analysis and demonstrates the reliability of the strategy through extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a big problem in combining different types of data (like images, text, audio) into one model. This is called “modality imbalance” because some types of data are more important than others. To fix this, the researchers came up with a new way to update the model’s parameters that takes into account how important each type of data is. They call it Adaptively Mask Subnetworks Considering Modal Significance (AMSS). It uses special tools like mutual information rates and non-uniform adaptive sampling to make sure all types of data are treated fairly. |
Keywords
* Artificial intelligence * Mask * Multi modal * Optimization
