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A PyTorch implementation for paper Unsupervised Domain Adaptation by Backpropagation
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add annotation to original gtsrb dataloader. 		5 years ago
core add l2 norm. 5 years ago
datasets add annotation to original gtsrb dataloader. 5 years ago
experiments change eval step to 1 5 years ago
models add syndigits-svhn experiment. 5 years ago
utils add syndigits-svhn experiment. 5 years ago
.gitignore minor update, remove some params 7 years ago
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requirements.txt add requirements 5 years ago

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 3.6
  • PyTorch 1.0

Note

  • 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()
    • not successful
  • AlexModel
    • not successful, mainly due to the preprain model difference

Result

MNIST-MNISTM SVHN-MNIST Amazon-Webcam SYNDIGITS-SVHN SYNSIGNS-GTSRB
Source Only 0.5225 0.5490 0.6420 0. 0.
DANN(paper) 0.7666 0.7385 0.7300 0.9109 0.7900
This Repo Source Only - - - 0. 0.
This Repo 0.8400 0.7339 0.6528 0.8200 0.6200

Credit