Mixup_for_UDA

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Implementation of Adversarial Domain Adaptation with Domain Mixup (AAAI 2020 Oral).

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- main.py
- models.py
- README.md
- trainer_mixed.py
- utils.py
- model.png
- visda_results.png
- data_loader.py
- models.py
- README.md
- RevGrad.py
- RevGrad_mixup.py
README.md

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# Adversarial Domain Adaptation with Domain Mixup

This is the implementation of Adversarial Domain Adaptation with Domain Mixup in PyTorch. This work is accepted as Oral presentation at AAAI 2020.

#### Adversarial Domain Adaptation with Domain Mixup: [[Paper (arxiv)]](https://arxiv.org/abs/1912.01805).

## Getting Started

* We combine Domain Mixup strategy with a classical adversarial domain adaptation method, [RevGrad](https://arxiv.org/abs/1409.7495v2), to showcase its effectiveness on boosting feature alignment. Details are presented in the [Mixup_RevGrad](https://github.com/ChrisAllenMing/Mixup_for_UDA/tree/master/Mixup_RevGrad) folder.
* The proposed DM-ADA approach utilizes a VAE-GAN based framework and performs Domain Mixup on both pixel and feature level. Details are presented in the [DM-ADA](https://github.com/ChrisAllenMing/Mixup_for_UDA/tree/master/DM-ADA) folder. Some typical generations from source, target and mixup features are as follows (VisDA-2017 dataset is employed).

## Citation

If this work helps your research, please cite the following paper.
```
@inproceedings{xu2020adversarial,
  author    = {Minghao Xu and Jian Zhang and Bingbing Ni and Teng Li and Chengjie Wang and Qi Tian and Wenjun Zhang},
  title     = {Adversarial Domain Adaptation with Domain Mixup},
  booktitle = {The Thirty-Fourth AAAI Conference on Artificial Intelligence},
  pages     = {6502--6509},
  publisher = {AAAI Press},
  year      = {2020}
}
```

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