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A PyTorch implementation for paper Unsupervised Domain Adaptation by Backpropagation
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README.md

PyTorch-DANN

A PyTorch implementation for paper Unsupervised Domain Adaptation by Backpropagation

InProceedings (icml2015-ganin15)
Ganin, Y. & Lempitsky, V.
Unsupervised Domain Adaptation by Backpropagation
Proceedings of the 32nd International Conference on Machine Learning, 2015

Environment

  • Python 3.8.5
  • PyTorch 1.6.0

Note

Before running the training code, make sure that DATASETDIR environment variable is set to dataset directory.

  • MNISTmodel()
    • basically the same network structure as proposed in the paper, expect for adding dropout layer in feature extractor
    • large gap exsits between with and w/o dropout layer
    • better result than paper
  • SVHNmodel()
    • network structure proposed in the paper may be wrong for both 32x32 and 28x28 inputs
    • change last conv layer's filter to 4x4, get similar(actually higher) result
  • GTSRBmodel()
  • AlexModel
    • not successful, mainly due to the pretrained model difference
  • ResNet50
    • Better and more stable results than AlexNet.

Result

MNIST-MNISTM SVHN-MNIST SYNDIGITS-SVHN SYNSIGNS-GTSRB
Source Only 0.5225 0.5490 0.8674 0.7900
DANN(paper) 0.7666 0.7385 0.9109 0.8865
This Repo Source Only - - - 0.9100
This Repo 0.8400 0.7339 0.8200 -
AMAZON-WEBVCAM DSLR-WEBCAM WEBCAM-DSLR
Source Only 0.6420 0.9610 0.9780
DANN(paper) 0.7300 0.9640 0.9920
This Repo Source Only - - -
This Repo 0.6528 - -

Credit