Specialized Foundation Models Struggle to Beat Supervised Baselines

Zongzhe Xu, Ritvik Gupta, Wenduo Cheng, Alexander Shen, Junhong Shen, Ameet Talwalkar, Mikhail Khodak·November 05, 2024

Summary

The text discusses the performance of foundation models in specialized domains like genomics, satellite imaging, and time series analysis. Despite recent advancements, simple supervised models often match or outperform these large-scale pre-trained models. The study highlights the need for robust baseline comparisons and efficient open-source workflows for evaluation. Foundation models, while powerful, have not fully translated their benefits to specialized areas, indicating that traditional supervised learning methods remain competitive. The text emphasizes the importance of domain-specific baselines and the potential for domain-aware model development using methods like neural architecture search and automated hyperparameter tuning. Various research papers and preprints are mentioned, focusing on advancements in geospatial AI, DNA-language modeling, time series forecasting, and machine learning applications across diverse fields.

Key findings

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