DAN

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.

  Requirement

• python 3

• pytorch 1.0

  Usage

1. You can download Office31 dataset here. And then unrar dataset in ./dataset/.
2. You can change the source_name and target_name in DAN.py to set different transfer tasks.
3. Run python DAN.py.

   Results on Office31

   | Method | A - W | D - W | W - D | A - D | D - A | W - A | Average | | —- | —- | —- | —- | —- | —- | —- | —- |

| DANori | 83.8±0.4 | 96.8±0.2 | 99.5±0.1 | 78.4±0.2 | 66.7±0.3 | 62.7±0.2 | 81.3 |

| DANlast | 81.6±0.7 | 97.2±0.1 | 99.5±0.1 | 80.0±0.7 | 66.2±0.6 | 65.6±0.4 | 81.7 |

| DANmax | 82.6±0.7 | 97.7±0.1 | 100.0±0.0 | 83.1±0.9 | 66.8±0.3 | 66.6±0.4 | 82.8 |

Note that the results DANori comes from paper which has the same author as DAN. The DANlast is the results of the last epoch, and DANmax is the results of the max results in all epoches. Both DANlast and DANmax are run by myself with the code.