This is the repo for reproducing the results in the paper Episodic Training for Domain Generalization.

Please download the data from https://drive.google.com/open?id=0B6x7gtvErXgfUU1WcGY5SzdwZVk and use the official train/val split.

We use the pytorch pretrained ResNet-18 model from https://download.pytorch.org/models/resnet18-5c106cde.pth

verified on

GPU GeForce RTX 2080 Ti
pytorch 1.0.0
Python 3.7.3
Ubuntu 16.04.6

and

GPU TITAN X (Pascal)
pytorch 0.4.1
Python 2.7
Scientific Linux 7.6

sh run_main_epi_fcr.sh #data_folder #model_path
sh run_main_agg.sh #data_folder #model_path

each point is the average performance of 20 runs on VLCS

lambda_1	lambda_2	lambda_3

If you consider using this code or its derivatives, please consider citing:

@InProceedings{Li_2019_ICCV,
author = {Li, Da and Zhang, Jianshu and Yang, Yongxin and Liu, Cong and Song, Yi-Zhe and Hospedales, Timothy M.},
title = {Episodic Training for Domain Generalization},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}

When working with a different enviroment, you can get different results and need to tune the hyper parameters yourself.