NeurIPS 2020

Your Classifier can Secretly Suffice Multi-Source Domain Adaptation

Meta Review

This paper proposes a simple self-training mechanism for multi-source domain adaptation. In a pre-training step, the features extracted by the deep learning networks from different sources are aligned using pseudo labels generated by independent softmax classifiers. All the reviewers agree that one of the strong contributions of this paper is the detailed experimental analysis that provide good insights to the reader on the topic of multi-source domain adaptation. This compensates for the limited novelty in the components used in this model such as multiple softmax classifiers which have been employed in related context even if they are not specifically for multi-source domain adaptation. Reviewers have highlighted a few shortcomings that could be improved in the final version of the paper and the authors have agreed to doing so in the response.