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Similarity of Pre-trained and Fine-tuned Representations

2022-07-19 12:23:08
Thomas Goerttler, Klaus Obermayer
arXiv_AI
         
Abstract

In transfer learning, only the last part of the networks - the so-called head - is often fine-tuned. Representation similarity analysis shows that the most significant change still occurs in the head even if all weights are updatable. However, recent results from few-shot learning have shown that representation change in the early layers, which are mostly convolutional, is beneficial, especially in the case of cross-domain adaption. In our paper, we find out whether that also holds true for transfer learning. In addition, we analyze the change of representation in transfer learning, both during pre-training and fine-tuning, and find out that pre-trained structure is unlearned if not usable.

Abstract (translated)

URL

https://arxiv.org/abs/2207.09225

PDF

https://arxiv.org/pdf/2207.09225.pdf

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