Summary of How to Merge Your Multimodal Models Over Time?, by Sebastian Dziadzio et al.
How to Merge Your Multimodal Models Over Time?
by Sebastian Dziadzio, Vishaal Udandarao, Karsten Roth, Ameya Prabhu, Zeynep Akata, Samuel Albanie, Matthias Bethge
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 introduces a novel framework called TIME (Temporal Integration of Model Expertise) that enables the integration of expert models from diverse tasks and domains into a single, more capable model. This framework addresses the challenge of temporal model merging, where new tasks and domains emerge progressively over time. The authors propose a unified approach that defines temporal model merging across three axes: initialization phase, deployment phase, and merging technique. They study this framework on the FoMo-in-Flux benchmark, exploring the impact of model sizes, compute budgets, and learning horizons on the effectiveness of temporal model merging. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to combine expert models from different areas into one better model. This is called “temporal model merging” because it happens over time as new tasks and domains come up. The authors developed a special framework called TIME that helps merge these models in three stages: starting, deploying, and combining. They tested this on the FoMo-in-Flux benchmark to see how different factors like model size, computer power, and learning pace affect the results. |
Keywords
» Artificial intelligence » Temporal model
