DTA
Official implementation of Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation, to be presented at ICCV 2019.
Setup
Environment
Create a new python virtual environment, and install the packages specified in requirements.txt:
pip install -r requirements.txt
The code has been tested on Python 3.6.8 with CUDA 9.0 on 4 Titan Xp GPUs.
Download Data
Download the VisDA-2017 Classification
data into ./data:
cd ./data
wget http://csr.bu.edu/ftp/visda17/clf/train.tar
tar xvf train.tar
wget http://csr.bu.edu/ftp/visda17/clf/validation.tar
tar xvf validation.tar
Running the code
We provide two basic configurations under ./configs/
for ResNet-101 and ResNet-50 models.
python main.py --config_path ./configs/resnet101_dta_vat.json
python main.py --config_path ./configs/resnet50_dta_vat.json
Hyper-parameters can be changed directly from the command line as well. For example, to run the ResNet-101 model without VAT:
python main.py --config_path ./configs/resnet101_dta_vat.json --use_vat False
Tensorboard logs are saved under the path specified in ./args.experiment_dir/args.experiment_description.
Copyright
All codes on this page are copyrighted by Naver Labs and published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License. You must attribute the work in the manner specified by the author. You may not use the work for commercial purposes, and you may only distribute the resulting work under the same license if you alter, transform, or create the work.
Acknowledgement
Citation
@InProceedings{Lee_2019_ICCV,
author = {Lee, Seungmin and Kim, Dongwan and Kim, Namil and Jeong, Seong-Gyun},
title = {Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}