Self-Supervised Domain Adaptation with Consistency Training
This addresses domain adaptation for image classification, but it is incremental as it builds on existing self-supervised and consistency methods.
The paper tackles unsupervised domain adaptation for image classification by using a self-supervised pretext task with image rotation and consistency training to learn target-domain-aware features, achieving state-of-the-art performance on classical benchmarks.
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.