Group Robust Classification Without Any Group Information

This PyTorch repository contains code for the reproduction of experiments presented at the NeurIPS 2023 paper.

Datasets

Please find instructions for the benchmarks we consider in the paper below:

• For the sMPI3D task, which is based on the MPI3D robotics dataset for disentanglement , please follow the instructions provided in /ula/data/mpi3d.py Python file. The same file can be used to generate the systematic splits.
• For the colored MNIST (cMNIST) and corrupted CIFAR10 (cCIFAR10) tasks, please follow the instructions provided by the DFA code repository.
• For the CelebA and Waterbirds tasks, instructions can be found at the Just-Train-Twice code repository.

Execute

In /scripts directory, you can find two example scripts to reproduce the uLA training and validation methodology. First, execute the bash script mocov2plus.py in order to pretrain a base network on the selected dataset. Please find instructions inside. Training will output checkpoints for the pretrained network in an experiment logging directory. Second, use a checkpoint for the pretrained base network to execute the ula.py script. This script will instantiate the base network, train a proxy for the bias variable by fitting a linear layer on top of the extracted representation, and then use the bias proxy to train a debiased network with logit adjustment and to perform bias-unsupervised validation on the debiased network, as it trains, at each epoch.

Code base is built upon the solo-learn library for self-supervised learning algorithms. We make its license available in the LICENSE_solo_learn file.