MLDG
This is a code sample for the paper "Learning to Generalize: Meta-Learning for Domain Generalization" https://arxiv.org/pdf/1710.03463.pdf
This code is the MLP version of MLDG with one-hidden layer, whose inputs are the features extracted for PACS. The baseline is the one for the sanity check without the meta-train and meta-val losses.
Requirements
Python 2.7
Pytorch 0.3.1
Run the baseline
Please download the data first, the data is the deep features extracted from ImageNet pretrained ResNet18, then
sh run_baseline.sh 'data_root/' # data_root is the folder path where you download your data to.
Run the MLDG
sh run_mldg.sh 'data_root/'
Bibtex
@inproceedings{Li2018MLDG,
title={Learning to Generalize: Meta-Learning for Domain Generalization},
author={Li, Da and Yang, Yongxin and Song, Yi-Zhe and Hospedales, Timothy},
booktitle={AAAI Conference on Artificial Intelligence},
year={2018}
}
Your own data
Please tune the meta_step_scale for your own data, this parameter is related to alpha in paper which should be tuned for different cases.