Surprisingly simple semi-supervised domain adaptation with pretraining and consistency
Mishra, Samarth; Saenko, Kate; Saligrama, Venkatesh
Visual domain adaptation involves learning to classify
images from a target visual domain using labels available
in a different source domain. A range of prior work uses
adversarial domain alignment to try and learn a domain invariant
feature space, where a good source classifier can
perform well on target data. This however, can lead to errors
where class A features in the target domain get aligned
to class B features in source. We show that in the presence
of a few target labels, simple techniques like selfsupervision
(via rotation prediction) and consistency regularization
can be effective without any adversarial alignment
to learn a good target classifier. Our Pretraining and
Consistency (PAC) approach, can achieve state of the art
accuracy on this semi-supervised domain adaptation task,
surpassing multiple adversarial domain alignment methods,
across multiple datasets. Notably, it outperforms all
recent approaches by 3-5% on the large and challenging
DomainNet benchmark, showing the strength of these simple
techniques in fixing errors made by adversarial alignment
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