Cross-Domain Feature Augmentation for Domain Generalization

This repository contains the implementation of the XDomainMix algorithm and empirical studies in Cross-Domain Feature Augmentation for Domain Generalization in IJCAI 2024. The arXiv version of the paper and the supplementary materials can be found at https://arxiv.org/abs/2405.08586.

Overview

XDomainMix performs feature augmentation to increase sample diversity while emphasizing the learning of invariant representations to achieve domain generalization. We decompose features into class-generic, class-specific, domain-generic, and domain-specific components, based on feature semantics' correlation with class and domain. Feature augmentation is performed by changing domain-specific components of a feature while preserving class-specific components.

On benchmark datasets, we achieve the following results:

Method	Camelyon17	FMoW	PACS	TerraIncognita	DomainNet
ERM	70.3±6.4	32.3±1.3	85.5±0.2	46.1±1.8	43.8±0.1
GroupDRO	68.4±7.3	30.8±0.8	84.4±0.8	43.2±1.1	33.3±0.2
RSC	77.0±4.9	32.6±0.5	85.2±0.9	46.6±1.0	38.9±0.5
MixStyle	62.6±6.3	32.9±0.5	85.2±0.3	44.0±0.7	34.0±0.1
DSU	69.6±6.3	32.5±0.6	85.5±0.6	41.5±0.9	42.6±0.2
LISA	77.1±6.5	35.5±0.7	83.1±0.2	47.2±1.1	42.3±0.3
Fish	74.7±7.1	34.6±0.2	85.5±0.3	45.1±1.3	42.7±0.2
XDomainMix	80.9±3.2	35.9±0.8	86.4±0.4	48.2±1.3	44.0±0.2

Implementation

The code is based on DomainBed by Gulrajani and Lopez-Paz, 2020, and LISA by Yao et al., 2022.

The dependencies in requirments.txt should be installed to run the experiments.

Training to get domain generalization performance

Camelyon and FMoW dataset

• under wilds/ directory, run

python main.py --dataset {dataset_name} --algorithm xdomain_mix --data-dir '/my/datasets/path'

PACS, TerraIncognita and DomainNet dataset (based on the README of DomainBed)

• download data by running

python -m domainbed.scripts.download --data_dir=/my/datasets/path

• train a model

python -m domainbed.scripts.train --algorithm xdomain_mix --data_dir /my/datasets/path --dataset {dataset_name} --test_envs 0 

• alternatively, launch a sweep that trains multiple models

python -m domainbed.scripts.sweep launch\
       --data_dir=/my/datasets/path\
       --output_dir=/my/sweep/output/path\
       --command_launcher multi_gpu\
       --algorithms xdomain_mix\
       --datasets PACS TerraIncognita DomainNet\
       --n_hparams 1\
       --n_trials 3\
       --single_test_envs

• collect result from a sweep

python -m domainbed.scripts.collect_results --input_dir=/my/sweep/output/path

Model invariance

Representation invariance

python -m feature_invariance.invariance_feature \
       --dataset {dataset_name} \
       --input_dir /my/sweep/path \
       --result_file /path/to/store/result/file

Predictor invariance

python -m feature_invariance.invariance_predictor \
       --dataset {dataset_name} \
       --input_dir /my/sweep/path \
       --result_file /path/to/store/result/file 

Feature divergence

python -m feature_divergence.augmentation_divergence \
       --dataset {dataset_name} \
       --data_dir /my/dataset/path \
       --output_dir /path/to/store/result/file \
       --algorithm xdomain_mix

Feature importance

python -m feature_importance.eval_feature_importance \
       --dataset {dataset_name} \
       --data_dir /my/dataset/path  \
       --output_dir /saved/model/path

Feature visualization

Specify data dir (path to dataset) and model dir (path to saved model) in the notebooks