Summary of A Practitioner’s Guide to Continual Multimodal Pretraining, by Karsten Roth et al.
A Practitioner’s Guide to Continual Multimodal Pretraining
by Karsten Roth, Vishaal Udandarao, Sebastian Dziadzio, Ameya Prabhu, Mehdi Cherti, Oriol Vinyals, Olivier Hénaff, Samuel Albanie, Matthias Bethge, Zeynep Akata
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 Multimodal foundation models are essential for various applications at the intersection of vision and language. However, these models become outdated over time despite being pre-trained on extensive data. To address this issue, research has mainly explored two scenarios: infrequent updates on large-scale new data or frequent, sample-level updates. In reality, model deployment often falls between these two extremes, requiring adaptation to specific subdomains, tasks, or concepts throughout a model’s life cycle. This paper introduces FoMo-in-Flux, a continual multimodal pretraining benchmark that simulates real-world deployment requirements and constraints. Using this benchmark, the authors explore the complex landscape of practical continual pretraining through various perspectives: data-centric, method-centric, meta-learning rate schedules, mechanistic design choices, and model scaling. The results provide a practitioner’s guide to continual multimodal pretraining for real-world deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to keep computer models updated when they’re used in different situations over time. These models are important for tasks like image recognition and language translation. Right now, researchers are trying to find ways to update these models quickly and efficiently. The problem is that real-world scenarios often require updating the models in a specific way, but current methods don’t work well in these situations. To address this issue, the authors created a test bed called FoMo-in-Flux that simulates how models are used in real-life applications. They then explored different ways to update these models using this test bed and found some effective approaches. The results provide guidance for people who want to use these models in their own projects. |
Keywords
» Artificial intelligence » Meta learning » Pretraining » Translation
