Specialized Foundation Models struggle to beat Supervised Baselines

ICLR 2025 Conference Submission12799 Authors

Keywords: foundation models, supervised learning, neural architecture search, hyperparameter optimization, convolutional networks, autoregressive models, genomics, satellite imaging, time series

TL;DR: Foundation models in specialized domains have in many cases not yet surpassed much smaller supervised models that are trained on much less data.

Abstract: Following its success for vision and text, the “foundation model” (FM) paradigm—pretraining large models on massive data, then fine-tuning on target tasks—has rapidly expanded to domains in the sciences, engineering, healthcare, and beyond. Has this achieved what the original FMs accomplished, i.e. the supplanting of traditional supervised learning in their domains? To answer we look at three modalities—genomics, satellite data, and time series—with multiple recent FMs and compare them to a standard supervised learning workflow: model development, hyperparameter tuning, and training, all using only data from the target task. Across those three specialized domains, we find that it is consistently possible to train simple supervised models—no more complicated than a lightly modified wide ResNet or UNet—that match or even outperform the latest foundation models. Our work demonstrates that the benefits of large-scale pretraining have yet to be realized in many specialized areas, reinforces the need to compare new FMs to strong, well-tuned baselines, and introduces two new, easy-to-use, open-source, and automated workflows for doing so.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 12799