A PyTorch implementation of 'Learning Transferable Features with Deep Adaptation Networks'. The contributions of this paper are summarized as follows.

• They propose a novel deep neural network architecture for domain adaptation, in which all the layers corresponding to task-specific features are adapted in a layerwise manner, hence benefiting from “deep adaptation.”
• They explore multiple kernels for adapting deep representations, which substantially enhances adaptation effectiveness compared to single kernel methods. Our model can yield unbiased deep features with statistical guarantees.