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This is the repo for the paper "Episodic Training for Domain Generalization" https://arxiv.org/abs/1902.00113

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Episodic-DG

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

Data

Please download the data from https://drive.google.com/drive/folders/0B6x7gtvErXgfUU1WcGY5SzdwZVk?resourcekey=0-2fvpQY_QSyJf2uIECzqPuQ&usp=sharing and use the official train/val split.

ImageNet pretrained model

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

Enviroments

verified on

GPU GeForce RTX 2080 Ti
pytorch 1.0.0
Python 3.7.3
Ubuntu 16.04.6

Method Art Cartoon Photo Sketch Ave.
AGG 76.1 75.2 94.9 69.7 79.0
Epi-FCR 79.6 76.8 93.7 77.1 81.8

and

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

Method Art Cartoon Photo Sketch Ave.
AGG 77.6 73.9 94.4 70.3 79.1
Epi-FCR 82.1 77.0 93.9 73.0 81.5

Run

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

Sensitivity of loss weights

each point is the average performance of 20 runs on VLCS

lambda_1 lambda_2 lambda_3

Reference

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}
}

Note

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

About

This is the repo for the paper "Episodic Training for Domain Generalization" https://arxiv.org/abs/1902.00113

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