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This is a PyTorch implementation of the IJCNN 2019 paper:

Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning

The contributions of this paper are summarized as follows.

  1. We present TCP as a unified approach for accelerating deep unsupervised domain adaptation models. TCP is a generic, accurate, and efficient compression method that can be easily implemented by most deep learning libraries.
  2. TCP is able to reduce negative transfer by considering the cross-domain distribution discrepancy using the proposed Transfer Channel Evaluation module.
  3. Extensive experiments on two public UDA datasets demonstrate the significant superiority of TCP.
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