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Code for NeurIPS 2022 Paper: Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors

This repository includes a PyTorch implementation of the NeurIPS 2022 paper Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors authored by Qixun Wang, Yifei Wang, Zhu Hong, and Yisen Wang.

The codes are in "domainbed" folder.

Quick Start

To get the sweep result on a certain datatset of an algorithm, try running:

python -m domainbed.scripts.sweep launch\
              --data_dir=/your_path_to_data/PACS\
              --output_dir=./domainbed/results/your_result_path/\
              --algorithms MAT\
              --command_launcher multi_gpu\
              --datasets PACS\
              --single_test_envs\
              --n_hparams 8\
              --n_trials 3\
              --kdelta_lower_bound 10\
              --kdelta_upper_bound 10\
              --kat_alpha_step_lb -3\
              --kat_alpha_step_ub -2\
              --at_eps_lb 0.1\
              --at_eps_ub 0.1\
              --at_alpha_lb -1\
              --at_alpha_ub -1\
              --steps 8000\
              --is_cmnist 0\
              --wd_lb 1e-3\
              --wd_ub 1e-3\
              --gpus 2

This will launch a sweep of MAT on PACS dataset. The script will train models with 8 different sets of hyperparameters and select the optimal model parameters for evaludation using both training-domain validation and test-domain validation. The whole process will run 3 random trials and average result will be reported. Argument --out_put_dir specifies where the evaluation results and checkpoints will be stored at. --data_dir is the dataset direction.

To view the results, run:

python -m domainbed.scripts.collect_results\
       --input_dir=./domainbed/results/your_result_path

Arguments

When conducting a sweep, you can specify the hyperparameters of the algorithm. We list the correspondence of some main hyperparameters and their argument name, so you can adjust them at will. 'lb'/'ub' indicate the lower/upper bound of the search space. The range in parentheses indicates that the parameter refers to a power of 10, not the parameter itself.

perturbation radius $\epsilon$ ---- at_eps_lb/at_eps_ub

FGSM step size $\gamma$ ---- at_alpha_lb/at_alpha_ub ($[10^{at_alpha_lb},10^{at_alpha_ub}]$)

MAT perturbation number $k$ ---- kdelta_lower_bound/kdelta_upper_bound

MAT alpha learning rate $\eta$ ---- kat_alpha_step_lb/kat_alpha_step_ub ($[10^{kat_alpha_step_lb},10^{kat_alpha_step_ub}]$)

LDAT rank $l$ ---- cb_rank_lb/cb_rank_ub

LDAT $A$ learning rate $\rho_A$ ---- A_lr_lb/A_lr_ub ($[10^{A_lr_lb},10^{A_lr_ub}]$)

LDAT $B$ learning rate $\rho_B$ ---- B_lr_lb/B_lr_ub ($[10^{B_lr_lb},10^{B_lr_ub}]$)

Network learining rate $r$ ---- lr_lb/lr_ub

Is/isn't CMNIST dataset ---- is_cmnist 1/0

Total training epochs ---- steps

Test on every domain ---- single_test_envs

Test on specified domains ---- test_domains 0,1,2

The default value of all the parameters is in hparams_registry.py.

This code is inherited from Domainbed https://github.com/facebookresearch/DomainBed. We implemented our algorithms MAT and LDAT in algorithms.py.

Citing this Work

If you find this work useful, please cite the accompanying paper:

@inproceedings{wang2022improving,
  title={Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors},
  author={Wang, Qixun and Wang, Yifei and Zhu, Hong and Wang, Yisen},
  booktitle={NeurIPS},
  year={2022}
}