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/drive/folders/0B6x7gtvErXgfUU1WcGY5SzdwZVk?resourcekey=0-2fvpQY_QSyJf2uIECzqPuQ&usp=sharing 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
| 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 |
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.
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