Feed-Forward Source-Free Domain Adaptation via Class Prototypes
Ondrej Bohdal Da Li Timothy Hospedales
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.