Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How

• URL: http://arxiv.org/abs/2306.03828v4
• Date: Thu, 22 Feb 2024 20:28:12 GMT
• Title: Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
• Authors: Sebastian Pineda Arango, Fabio Ferreira, Arlind Kadra, Frank Hutter, Josif Grabocka
• Abstract summary: We propose a methodology that jointly searches for the optimal pretrained model and the hyperparameters for finetuning it. Our method transfers knowledge about the performance of many pretrained models on a series of datasets. We empirically demonstrate that our resulting approach can quickly select an accurate pretrained model for a new dataset.
• Score: 62.467716468917224
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the ever-increasing number of pretrained models, machine learning practitioners are continuously faced with which pretrained model to use, and how to finetune it for a new dataset. In this paper, we propose a methodology that jointly searches for the optimal pretrained model and the hyperparameters for finetuning it. Our method transfers knowledge about the performance of many pretrained models with multiple hyperparameter configurations on a series of datasets. To this aim, we evaluated over 20k hyperparameter configurations for finetuning 24 pretrained image classification models on 87 datasets to generate a large-scale meta-dataset. We meta-learn a multi-fidelity performance predictor on the learning curves of this meta-dataset and use it for fast hyperparameter optimization on new datasets. We empirically demonstrate that our resulting approach can quickly select an accurate pretrained model for a new dataset together with its optimal hyperparameters.

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