Summary of Flex-moe: Modeling Arbitrary Modality Combination Via the Flexible Mixture-of-experts, by Sukwon Yun et al.
Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts
by Sukwon Yun, Inyoung Choi, Jie Peng, Yangfan Wu, Jingxuan Bao, Qiyiwen Zhang, Jiayi Xin, Qi Long, Tianlong Chen
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes Flex-MoE, a novel framework for multimodal learning that can seamlessly integrate data from diverse sources, including images, text, and personalized records. The approach addresses the limitation of existing frameworks by accommodating arbitrary modality combinations while maintaining robustness to missing data. The core idea is to first address missing modalities using a new missing modality bank, followed by a Sparse MoE framework that injects generalized knowledge through a generalized router (-Router) and specializes in handling fewer modality combinations. The paper evaluates Flex-MoE on the ADNI and MIMIC-IV datasets, demonstrating its effectiveness in modeling arbitrary modality combinations in diverse missing modality scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Flex-MoE is a new way to learn from different types of data together. This can be helpful when some types of data are missing, but there’s still other information available. The approach works by first filling in the gaps and then using that filled-in data to make predictions. It’s like having a special tool that can handle different combinations of data, even if some parts are missing. |
