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README.md

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-# SSL-DA
-Semi-supervised domain adaptation rep
+# [Semi-supervised Domain Adaptation via Minimax Entropy (ICCV 2019)](https://arxiv.org/pdf/1904.06487.pdf)
+
+## 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+At the moment (8/18/2019), we do not publish all data of DomainNet because we hold a [competition](http://ai.bu.edu/visda-2019/) and some domains are used there.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
+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}
+}
+```
+
+
+
+
+