Semi-supervised Domain Adaptation via Minimax Entropy

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Deep LearningSSDA_MME
Overview

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At the moment (8/18/2019), we do not publish all data of DomainNet because we hold a competition and some domains are used there.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}
}
Owner
Vision and Learning Group
Vision and Learning Group
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