  We consider the problem of unsupervised domain adaptation for image
classification. To learn target-domain-aware features from the unlabeled data,
we create a self-supervised pretext task by augmenting the unlabeled data with
a certain type of transformation (specifically, image rotation) and ask the
learner to predict the properties of the transformation. However, the obtained
feature representation may contain a large amount of irrelevant information
with respect to the main task. To provide further guidance, we force the
feature representation of the augmented data to be consistent with that of the
original data. Intuitively, the consistency introduces additional constraints
to representation learning, therefore, the learned representation is more
likely to focus on the right information about the main task. Our experimental
results validate the proposed method and demonstrate state-of-the-art
performance on classical domain adaptation benchmarks. Code is available at
https://github.com/Jiaolong/ss-da-consistency.
