Finding the Needle in the Pre-Trained Model Zoo

The Use of Rich Metadata and Graph Learning to Estimate Task Transferability

Master thesis (2024)

Authors

H.J. van der Wilk Electrical Engineering, Mathematics and Computer Science

Contributors

R. Hai Web Information Systems - EEMCS (mentor)
Z. Li Web Information Systems - EEMCS (mentor)
A. Anand Web Information Systems - EEMCS (graduation committee member)
Q. Song Embedded Systems - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
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Published Date

25-06-2024

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The democratization of machine learning through public repositories, often known as model zoos, has significantly increased the availability of pre-trained models for practitioners. However, this abundance can make it difficult to choose the most suitable pre-trained model for fine-tuning on new tasks. Although various methods have been proposed in the field of transferability estimation to address this issue, these methods can take hours to execute and may still fail to find the optimal pre-trained model for fine-tuning. By exploring a new graph learning-based approach to transferability estimation, we outperform state-of-the-art methods such as LogME, improving the accuracy of the best-predicted model by up to 31.5\% in less than 5 minutes.