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MEDIC

Please skip to our new repository MEDIC++ for faster execution speed and higher performance.

1. Introduction

This repository contains the implementation of the paper Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain Generalization:

# PACS
Known classes: ['dog', 'elephant', 'giraffe', 'guitar', 'horse', 'house']
Unknown classes: ['person']

2. Dataset Construction

The dataset needs to be divided into two folders for training and validation. We provide reference code for automatically dividing data using official split in data_list/split_kfold.py.

root_dir = "path/to/PACS"
instr_dir = "path/to/PACS_data_list"

3. Train

To run the training code, please update the path of the dataset in ml_open.py:

if dataset == 'PACS':	
    train_dir = 'path/to/PACS_train' # the folder of training data 
	val_dir = 'path/to/PACS_val' # the folder of validation data 
	test_dir = 'path/to/PACS_all' or ['path/to/PACS_train', 'path/to/PACS_val']

then simply run:

python ml_open.py --source-domain ... --target-domain ... --save-name ... --gpu 0

4. Evalution

To run the evaluation code, please update the path of the dataset in eval.py:

if dataset == 'PACS':
        root_dir = 'path/to/PACS_all' or ['path/to/PACS_train', 'path/to/PACS_val']

then simply run:

python eval.py --save-name ... --gpu 0