Semi-supervised Domain Adaptation via Minimax Entropy (ICCV 2019)
Install
pip install -r requirements.txt
The code is written for Pytorch 0.4.0, but should work for other version with some modifications.
Data preparation (DomainNet)
To get data, run
sh download_data.sh
The images will be stored in the following way.
./data/multi/real/category_name,
./data/multi/sketch/category_name
The dataset split files are stored as follows,
./data/txt/multi/labeled_source_images_real.txt,
./data/txt/multi/unlabeled_target_images_sketch_3.txt,
./data/txt/multi/validation_target_images_sketch_3.txt.
At the moment (8/18/2019), we do not publish all data of DomainNet because we hold a competition and some domains are used there.
With regard to office and office home dataset, store the image files in the following ways,
./data/office/amazon/category_name, ./data/office_home/Real/category_name,
We provide the split of office and office-home.
Training
To run training using alexnet,
sh run_train.sh gpu_id method alexnet
where, gpu_id = 0,1,2,3...., method=[MME,ENT,S+T].
Reference
This repository is contributed by Kuniaki Saito and Donghyun Kim If you consider using this code or its derivatives, please consider citing:
@article{saito2019semi,
title={Semi-supervised Domain Adaptation via Minimax Entropy},
author={Saito, Kuniaki and Kim, Donghyun and Sclaroff, Stan and Darrell, Trevor and Saenko, Kate},
journal={ICCV},
year={2019}
}
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