Which Model to Transfer? Finding the Needle in the Growing Haystack

• URL: http://arxiv.org/abs/2010.06402v2
• Date: Fri, 25 Mar 2022 08:27:57 GMT
• Title: Which Model to Transfer? Finding the Needle in the Growing Haystack
• Authors: Cedric Renggli, Andr\'e Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lucic
• Abstract summary: We provide a formalization of this problem through a familiar notion of regret. We show that both task-agnostic and task-aware methods can yield high regret. We then propose a simple and efficient hybrid search strategy which outperforms the existing approaches.
• Score: 27.660318887140203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transfer learning has been recently popularized as a data-efficient alternative to training models from scratch, in particular for computer vision tasks where it provides a remarkably solid baseline. The emergence of rich model repositories, such as TensorFlow Hub, enables the practitioners and researchers to unleash the potential of these models across a wide range of downstream tasks. As these repositories keep growing exponentially, efficiently selecting a good model for the task at hand becomes paramount. We provide a formalization of this problem through a familiar notion of regret and introduce the predominant strategies, namely task-agnostic (e.g. ranking models by their ImageNet performance) and task-aware search strategies (such as linear or kNN evaluation). We conduct a large-scale empirical study and show that both task-agnostic and task-aware methods can yield high regret. We then propose a simple and computationally efficient hybrid search strategy which outperforms the existing approaches. We highlight the practical benefits of the proposed solution on a set of 19 diverse vision tasks.

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