Feed-Forward Source-Free Domain Adaptation via Class Prototypes

• URL: http://arxiv.org/abs/2307.10787v1
• Date: Thu, 20 Jul 2023 11:36:45 GMT
• Title: Feed-Forward Source-Free Domain Adaptation via Class Prototypes
• Authors: Ondrej Bohdal, Da Li, Timothy Hospedales
• Abstract summary: We present a feed-forward approach that challenges the need for back-propagation based adaptation. Our approach is based on computing prototypes of classes under the domain shift using a pre-trained model.
• Score: 3.5382535469099436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Source-free domain adaptation has become popular because of its practical usefulness and no need to access source data. However, the adaptation process still takes a considerable amount of time and is predominantly based on optimization that relies on back-propagation. In this work we present a simple feed-forward approach that challenges the need for back-propagation based adaptation. Our approach is based on computing prototypes of classes under the domain shift using a pre-trained model. It achieves strong improvements in accuracy compared to the pre-trained model and requires only a small fraction of time of existing domain adaptation methods.

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