This project is the implementation of ``Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation'' in PyTorch, which is accepted by ECCV 2020.
The paper is available here: arXiv
- Python 3.6
- PyTorch 1.1.0 (any version higher than 0.4.0 should work)
- CUDA 9.0 & cuDNN 7.0.5
NOTE. We experimentally found that the Caffe pretrained model outperforms the PyTorch pretrained one. If you would like to evaluate our method with other backbones, a converted model from Caffe to PyTorch maybe favored.
To train the baseline model without target data, simply run:
python train.py --save_model --target $target_domain$ \
--checkpoint_dir $save_dir$
To train the full model of LtC-MSDA, simply run:
python train.py --use_target --save_model --target $target_domain$ \
--checkpoint_dir $save_dir$
P.S. When the ``--save model'' config is active, model's parameters, global prototypes and adjacency matrix will be stored.
To evaluate the LtC-MSDA model, you can run:
python test.py --target $target_domain$ --load_checkpoint $checkpoint_file$
If this work helps your research, please cite the following paper:
@inproceedings{ wang2020learning,
title = {Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation},
author = {Wang, Hang and Xu, Minghao and Ni, Bingbing and Zhang, Wenjun},
booktitle = {European Conference on Computer Vision},
year = {2020}
}
Also, this method has been extended into a journal work, and we will release the code of the novel ``MRF-MSDA'' in the journal version upon acceptance (most likely in a separate repository). It will be very kind of you if you can also cite our journal work:
@article{xu2022graphical,
title={Graphical modeling for multi-source domain adaptation},
author={Xu, Minghao and Wang, Hang and Ni, Bingbing},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2022},
publisher={IEEE}
}
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