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Adaptive Risk Minimization: Learning to Adapt to Domain Shift

Paper: https://arxiv.org/abs/2007.02931

The structure of this repo and the way certain details around the training loop and evaluation loop is set up is inspired by and adapted from the DomainBed repo and the Wilds repo.

Environment
Logging Results
Experiments Setup
- Train
- Evaluate

Environment

python version: 3.6.5

Using pip

pip install -r requirements.txt or pip3 install -r requirements.txt

Logging results.

Weights and Biases, which is an alternative to Tensorboard, is used to log results in the cloud. This is used for both training and evaluating on the test set. To get it running quickly without WandB, we have set --log_wandb 0 below. Much of the results will be printed in the console. We recommend using WandB which is free for researchers.

Data

Femnist The train/val/test data split used in the paper can be found here: https://drive.google.com/file/d/1xvT13Sl3vJIsC2I7l7Mp8alHkqKQIXaa/view?usp=sharing

CIFAR-C

Test data can be downloaded here: https://zenodo.org/record/2535967#.YCUsMukzZ0s
The training and validation split used in the paper can be found here: https://drive.google.com/file/d/1blM7LHGR62-dVJjNAScsJMlzeiQS9DX1/view?usp=sharing

TinyImg

Test data can be downloaded here: https://zenodo.org/record/2536630#.YCUsBOkzZ0s
The training and validation split used in the paper can be found here: https://drive.google.com/file/d/13hd39InVa5WqPUpuoJtl9kSSwyDFyFNc/view?usp=sharing

Experiments Setup

Showing example args for MNIST here. See all_commands.sh for more details.

1. Train

Shared args

SEEDS="0"
SHARED_ARGS="--dataset mnist --num_epochs 200 --n_samples_per_group 300 --epochs_per_eval 10 --seeds ${SEEDS} --meta_batch_size 6 --epochs_per_eval 10 --log_wandb 0 --train 1"

ERM

python run.py --exp_name erm $SHARED_ARGS

UW (Upweighted)

python run.py --uniform_over_groups 1 --exp_name uw $SHARED_ARGS

DRNN (Distributionally Robust Neural Networks)

python run.py --algorithm drnn --uniform_over_groups 1 --exp_name drnn $SHARED_ARGS

ARM-CML (Adaptive Risk Minimization - Contextual Meta-learner)

python run.py --algorithm ARM-CML --sampler group --uniform_over_groups 1 --n_context_channels 12 --exp_name arm_cml $SHARED_ARGS

ARM-LL (Adaptive Risk Minimization - Learned Loss)

python run.py --algorithm ARM-LL --sampler group --uniform_over_groups 1 --exp_name arm_ll $SHARED_ARGS

ARM-BN (Adaptive Risk Minimization - Batchnorm)

python run.py --algorithm ARM-LL --sampler group --uniform_over_groups 1 --exp_name arm_bn $SHARED_ARGS

CML ablation

python run.py --algorithm ARM-CML --sampler regular --experiment_name cml_ablation $SHARED_ARGS

2. Evaluate

Your trained models are saved in output/checkpoints/{dataset}_{exp_name}_{seed}_{datetime}/

An example of checkpoint could be:

output/checkpoints/mnist_erm_0_20200529-130211/best_weights.pkl

To evaluate a set of checkpoints, you run:

python run.py --eval_on test --test 1 --train 0 --ckpt_folders CKPT_FOLDER1 CKPT_FOLDER2 CKPT_FOLDER3 --log_wandb 0`

E.g., you could run

python run.py --eval_on test --test 1 -- train 0 --ckpt_folders mnist_erm_0_1231414 mnist_erm_1_1231434 mnist_erm_2_2_1231414 --log_wandb 0`

--ckpt_folders is a list of the folders

You can vary support size with --support_size.

Citation

If you find this codebase useful in your research, consider citing:

@inproceedings{arm,
    author={Zhang, M. and Marklund, H. and Dhawan, N. and Gupta, A. and Levine, S. and Finn, C.},
    title={Adaptive Risk Minimization: Learning to Adapt to Domain Shift},
    booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
    year={2021},
}

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