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, CIFARSTL, 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) .