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https://github.com/wogong/pytorch-dann

A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation
https://github.com/wogong/pytorch-dann

deep-learning domain-adaptation generative-adversarial-network pytorch

Last synced: 3 months ago
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A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation

Host: GitHub
URL: https://github.com/wogong/pytorch-dann
Owner: wogong
License: mit
Created: 2018-05-09T15:24:51.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2020-09-30T19:21:38.000Z (over 4 years ago)
Last Synced: 2024-10-12T21:08:32.602Z (4 months ago)
Topics: deep-learning, domain-adaptation, generative-adversarial-network, pytorch
Language: Jupyter Notebook
Homepage:
Size: 97.7 KB
Stars: 145
Watchers: 4
Forks: 19
Open Issues: 2
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # PyTorch-DANN

A PyTorch implementation for paper *[Unsupervised Domain Adaptation by Backpropagation](http://sites.skoltech.ru/compvision/projects/grl/)*

    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()`

- `AlexModel`

    - not successful, mainly due to the pretrained model difference

## 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

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