  Transferring knowledge across different datasets is an important approach to
successfully train deep models with a small-scale target dataset or when few
labeled instances are available. In this paper, we aim at developing a model
that can generalize across multiple domain shifts, so that this model can adapt
from a single source to multiple targets. This can be achieved by randomizing
the generation of the data of various styles to mitigate the domain mismatch.
First, we present a new adaptation to the CycleGAN model to produce stochastic
style transfer between two image batches of different domains. Second, we
enhance the classifier performance by using a self-ensembling technique with a
teacher 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 that
selfensembling is better than simple data augmentation with the newly generated
data and a single model trained this way can have the best performance across
all different transfer tasks.
