Fine-tuning from a collection of models pre-trained on different domains (a "model zoo") is emerging as a technique to improve test accuracy in the low-data regime. However, model selection, i.e. how to pre-select the right model to fine-tune from a model zoo without performing any training, remains an open topic. We use a linearized framework to approximate fine-tuning, and introduce two new baselines for model selection -- Label-Gradient and Label-Feature Correlation. Since all model selection algorithms in the literature have been tested on different use-cases and never compared directly, we introduce a new comprehensive benchmark for model selection comprising of: i) A model zoo of single and multi-domain models, and ii) Many target tasks. Our benchmark highlights accuracy gain with model zoo compared to fine-tuning Imagenet models. We show our model selection baseline can select optimal models to fine-tune in few selections and has the highest ranking correlation to fine-tuning accuracy compared to existing algorithms.
@article{arxiv.2102.00084,
title = {A linearized framework and a new benchmark for model selection for fine-tuning},
author = {Aditya Deshpande and Alessandro Achille and Avinash Ravichandran and Hao Li and Luca Zancato and Charless Fowlkes and Rahul Bhotika and Stefano Soatto and Pietro Perona},
journal= {arXiv preprint arXiv:2102.00084},
year = {2021}
}