Surgical Fine-Tuning Improves Adaptation to Distribution ShiftsDownload PDF

05 Oct 2022, 00:13 (modified: 11 Nov 2022, 04:11)NeurIPS 2022 Workshop DistShift PosterReaders: Everyone
Keywords: Transfer learning, fine-tuning, parameter freezing, distortion of pre-trained models
TL;DR: Selectively fine-tuning a subset of layers outperforms full fine-tuning when transferring to tasks with various distribution shifts.
Abstract: A common approach to transfer learning under distribution shift is to fine-tune the last few layers of a pre-trained model, preserving learned features while also adapting to the new task. This paper shows that in such settings, selectively fine-tuning a subset of layers (which we term surgical fine-tuning) matches or outperforms commonly used fine-tuning approaches. Moreover, the type of distribution shift influences which subset is more effective to tune: for example, for image corruptions, fine-tuning only the first few layers works best. We validate our findings systematically across seven real-world data tasks spanning three types of distribution shifts. Theoretically, we prove that for two-layer neural networks in an idealized setting, first-layer tuning can outperform fine-tuning all layers. Intuitively, fine-tuning more parameters on a small target dataset can cause information learned during pre-training to be forgotten, and the relevant information depends on the type of shift.
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