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Transferring knowledge across different datasets is an important approach tosuccessfully train deep models with a small-scale target dataset or when fewlabeled instances are available. In this paper, we aim at developing a modelthat can generalize across multiple domain shifts, so that this model can adaptfrom a single source to multiple targets. This can be achieved by randomizingthe generation of the data of various styles to mitigate the domain mismatch.First, we present a new adaptation to the CycleGAN model to produce stochasticstyle transfer between two image batches of different domains. Second, weenhance the classifier performance by using a self-ensembling technique with ateacher and student model to train on both original and generated data.Finally, we present experimental results on three datasets Office-31,Office-Home, and Visual Domain adaptation. The results suggest thatselfensembling is better than simple data augmentation with the newly generateddata and a single model trained this way can have the best performance acrossall different transfer tasks.

Unsupervised Domain Adaptation using Generative Models and Self-ensembling