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2.2 KiB
2.2 KiB
PyTorch-DANN
This repo is mostly based on https://github.com/wogong/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 | - | - |