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Bayesian Invariant Risk Minmization (BIRM)

The repo for Bayesian Invariant Risk Minimization, CVPR2022 (oral).

Our implementation is based on the source code of IRM, ColoredObject, and CifarMnist.

Requirements

Our code works with the following environment.

• python=3.7.0
• torch=1.3.1
• h5py==2.8.0

To install the necessary packages for the project, please run: pip install -r requirements.txt.

Quick Start (For Reproducing Results)

1. To perform BIRM on CMNIST (with 20K training data). Run the command sh auto_CMNIST.sh. The expected test accuracy is 67.0±1.8.
2. To perform BIRM on ColoredObject. First run sh prepare_coco_dataset.sh to download MSCOCO dataset and preprocess the images (it may take several hours, please be patient). Second run the command sh auto_CifarMnist.sh to train BRIM. The expected test accuracy is 78.1±0.6.
3. To perform BIRM on CifarMnist. Run the command sh auto_ColoredObject.sh. The expected test accuracy is 59.3±2.3.

Important arguments:

• dataset: chosen in CMNIST, ColoredObject and CifarMnist;
• l2_regularizer_weight: weight decay coeffient;
• lr: learning rate;
• opt: the optimizer. By default, we use adam for CMNIST and use sgd for ColoredObject and CifarMnist;
• data_num: (only valid for dataset CMNIST) the number of training data;
• penalty_weight: the weight of the BIRM penalty;
• penalty_anneal_iters: the steps that we trians ERM first, after which BIRM penalty will be applied.
• step_gamma: the ratio of step decay, 0.1 means the learning rate will decay by 0.1 at the middle of the training steps.

Datasets

Implemented Datasets

• CMNIST: the most popular dataset in IRM literatures. The invariant feature is the shape of the digit from MNIST and the spurious feature is the attached color.
• ColoredObject: Following Faruk-Ahmed, we construct coloredObject by superimposing objects extracted from MSCOCO on a colored background (spurious feature)
• CifarMnist: Following Shah, we construct each image in CifarMnist by by concatenating two component images: CIFAR-10 (invariant) and MNIST (spurious).

Refer to Section 5 of our paper for detailed discription of the datasets.

Use BIRM on Your Own Data

We provider interface for you to include your own data. You need to inherit the class IRMDataProvider, and re-implement the function fetch_train and fetch_test. The main function will call fetch_train to get training data for each step. fetch_train should return the following values:

• train_x: the feature tensor;
• train_y: the label tensor;
• train_g: the tensor contains values indicating which environmnets the data are from;
• train_c(optional): the tensor contains values indicating whether the spurious features align with the labels.

The structure of the return value of fetch_test are similar with fetch_train.

Contact Information

For help or issues using Bayesian Invariant Risk Minimization, please submit a GitHub issue.

For personal communication related to BayesianIRM, please contact Yong Lin (ylindf@connect.ust.hk).

Reference

If you use or extend our work, please cite the following paper:

@inproceedings{lin2022bayesian,
  title={Bayesian Invariant Risk Minimization},
  author={Lin, Yong and Dong, Hanze and Wang, Hao and Zhang, Tong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={16021--16030},
  year={2022}
}