Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

• URL: http://arxiv.org/abs/2202.10054v1
• Date: Mon, 21 Feb 2022 09:03:34 GMT
• Title: Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
• Authors: Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, Percy Liang
• Abstract summary: Fine-tuning can achieve worse accuracy than linear probing when the pretrained features are good and the distribution shift is large. We show theoretically that this tradeoff between ID and OOD accuracy arises even in a simple setting. Our analysis suggests that the easy two-step strategy of linear probing then full fine-tuning combines the benefits of both fine-tuning and linear probing.
• Score: 100.01469697743322
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When transferring a pretrained model to a downstream task, two popular methods are full fine-tuning (updating all the model parameters) and linear probing (updating only the last linear layer -- the "head"). It is well known that fine-tuning leads to better accuracy in-distribution (ID). However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large. On 10 distribution shift datasets (Breeds-Living17, Breeds-Entity30, DomainNet, CIFAR $\to$ STL, CIFAR10.1, FMoW, ImageNetV2, ImageNet-R, ImageNet-A, ImageNet-Sketch), fine-tuning obtains on average 2% higher accuracy ID but 7% lower accuracy OOD than linear probing. We show theoretically that this tradeoff between ID and OOD accuracy arises even in a simple setting: fine-tuning overparameterized two-layer linear networks. We prove that the OOD error of fine-tuning is high when we initialize with a fixed or random head -- this is because while fine-tuning learns the head, the lower layers of the neural network change simultaneously and distort the pretrained features. Our analysis suggests that the easy two-step strategy of linear probing then full fine-tuning (LP-FT), sometimes used as a fine-tuning heuristic, combines the benefits of both fine-tuning and linear probing. Empirically, LP-FT outperforms both fine-tuning and linear probing on the above datasets (1% better ID, 10% better OOD than full fine-tuning).

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