sdm-ddml's Introduction

DDML

This repository contains code for DDML. Some of the methods are adopted from the ARM. We use three datasets to do expeirments on, including rotated MNIST, affNIST and rotated Tiny ImageNet-C.

Environment

You can install required packages via pip.

pip3 install -r requirements.txt

Rotated MNIST Experiment Setting

We rotate the original MNIST dataset by different degrees under this setting.

Train:

python train.py --dataset mnist --experiment_name mnist_ddml --num_epochs 200 --n_test_per_dist 300 --epochs_per_eval 10 --epochs_per_eval 10 --log_wandb 0

Test: Use the checkpointed model from CKPT_FOLDER

python test.py --dataset mnist --eval_on test --ckpt_folders CKPT_FOLDER --log_wandb 0

AffNIST Experiment Setting

The dataset is accessible via this link (https://www.cs.toronto.edu/~tijmen/affNIST/).

Train:

python train.py --dataset affnist --experiment_name affnist_ddml --pretrained 1 --prediction_net resnet50 --num_epochs 200 --n_test_per_dist 300 --epochs_per_eval 10 --epochs_per_eval 10 --log_wandb 0

Test: Use the checkpointed model from CKPT_FOLDER

python test.py --dataset affnist --eval_on test --pretrained 1 --prediction_net resnet50 --ckpt_folders CKPT_FOLDER --log_wandb 0

Rotated Tiny ImageNet-C Experiment Setting.

The dataset is accessible from this link (https://github.com/hendrycks/robustness).

Train:

python train.py --dataset rimagenet --experiment_name rimage_ddml --pretrained 1 --prediction_net resnet50 --num_epochs 200 --n_test_per_dist 300 --epochs_per_eval 10 --epochs_per_eval 10 --log_wandb 0

Test: Use the checkpointed model from CKPT_FOLDER

python test.py --dataset rimagenet --eval_on test --pretrained 1 --prediction_net resnet50 --ckpt_folders CKPT_FOLDER --log_wandb 0

References

Zhang, M., Marklund, H., Gupta, A., Levine, S., & Finn, C. (2020). Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift. arXiv preprint arXiv:2007.02931.

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