Summary of Specialized Foundation Models Struggle to Beat Supervised Baselines, by Zongzhe Xu et al.
Specialized Foundation Models Struggle to Beat Supervised Baselines
by Zongzhe Xu, Ritvik Gupta, Wenduo Cheng, Alexander Shen, Junhong Shen, Ameet Talwalkar, Mikhail Khodak
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Genomics (q-bio.GN)
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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 “foundation model” (FM) paradigm has been widely applied beyond computer vision and text tasks. The FM involves pretraining large models on massive data and fine-tuning them on target tasks. Our study investigates whether this approach achieves the same results as traditional supervised learning in various domains, including genomics, satellite imaging, and time series analysis. We compare recent FMs to a standard supervised learning workflow using only data from the target task. Our findings show that simple supervised models can match or outperform latest foundation models across these three specialized domains. This work emphasizes the need for comparing new FMs to strong baselines and introduces two automated workflows for doing so. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have been trying a new way to train AI models called “foundation models”. They pretrain large models on lots of data, then fine-tune them for specific tasks. But how well does this work in different areas? We looked at three examples: genomics, satellite imaging, and time series analysis. We compared the foundation models to a more traditional way of training AI models using only the data from each task. Our results show that simple AI models can be just as good or even better than the latest foundation models in these specific areas. This study highlights the importance of comparing new AI models to strong baselines and introduces two easy-to-use tools for doing so. |
Keywords
» Artificial intelligence » Fine tuning » Pretraining » Supervised » Time series
