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.
@article{arxiv.2307.10787,
title = {Feed-Forward Source-Free Domain Adaptation via Class Prototypes},
author = {Ondrej Bohdal and Da Li and Timothy Hospedales},
journal= {arXiv preprint arXiv:2307.10787},
year = {2023}
}
Comments
ECCV 2022 Workshop on Out of Distribution Generalization in Computer Vision (OOD-CV)