Summary of Mm-lego: Modular Biomedical Multimodal Models with Minimal Fine-tuning, by Konstantin Hemker et al.
MM-Lego: Modular Biomedical Multimodal Models with Minimal Fine-Tuning
by Konstantin Hemker, Nikola Simidjievski, Mateja Jamnik
First submitted to arxiv on: 30 May 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 The paper presents Multimodal Lego (MM-Lego), a novel framework for fusing and merging unimodal encoders into a competitive multimodal model. MM-Lego addresses the limitations of existing multimodal fusion methods, which require end-to-end training, scale poorly with the number of modalities, or are topology-specific. The proposed wrapper enforces lightweight dimensionality assumptions between modalities, harmonizing their representations by learning features in the frequency domain. This allows for model merging with minimal signal interference. MM-Lego achieves competitive performance without fine-tuning and outperforms state-of-the-art results on six benchmarked multimodal biomedical tasks. The framework is modular, general-purpose, and can be used as a model merging method or fusion method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand different types of data, like images, sounds, and text, all at the same time. This paper proposes a new way to combine these different types of data into one powerful tool for learning. The tool, called Multimodal Lego (MM-Lego), can take many different kinds of data and merge them together without needing a lot of training or fine-tuning. MM-Lego is special because it can work with any type of data and doesn’t get stuck on just one way of processing information. This makes it very useful for learning about complex systems, like the human body. |
Keywords
» Artificial intelligence » Fine tuning
