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wogong
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core | 5 years ago | |
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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 3.6
- 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. | 0.7650 |
This Repo | 0.8400 | 0.7339 | 0.6528 | 0.8200 | 0.6200 |