  The pretrain-finetune paradigm usually improves downstream performance over
training a model from scratch on the same task, becoming commonplace across
many areas of machine learning. While pretraining is empirically observed to be
beneficial for a range of tasks, there is not a clear understanding yet of the
reasons for this effect. In this work, we examine the relationship between
pretrained vision transformers and the corresponding finetuned versions on
several benchmark datasets and tasks. We present new metrics that specifically
investigate the degree to which invariances learned by a pretrained model are
retained or forgotten during finetuning. Using these metrics, we present a
suite of empirical findings, including that pretraining induces transferable
invariances in shallow layers and that invariances from deeper pretrained
layers are compressed towards shallower layers during finetuning. Together,
these findings contribute to understanding some of the reasons for the
successes of pretrained models and the changes that a pretrained model
undergoes when finetuned on a downstream task.
