Official repo for Robust and Generalizable Visual Representation Learning via Random Convolutions (ICLR2021)
Update 05/10: Code for RandConv and training scripts on digits data are available now! Scripts for PACS and imagenet are on the way.
See requirements.txt
. Note that Pytorch v1.7 was used for testing.
- MNIST-C has to be manually downloaded from https://github.com/google-research/mnist-c. Unzip the data into ./data/MNIST-M or change the data path in
train_digits.py
. exp_mnist10k.sh
provided bash commands for reproduce digits experiments in the paper. You can select the specific settings by (un)commenting lines.bash exp_mnist10k.sh 0
will run selected settings on GPU 0.