ReadMe for the paper "Predicting with High Correlation Features": 
Version of softwares used:
1. Python 3.6.8
2. PyTorch 1.0.0
Commands:
1. Generate Colored MNIST:
python gen_color_mnist.py
2. Sample command to run Correlation based regularization:
python main.py --dataset fgbg_cmnist_cpr0.5-0.5 --seed 0 --root_dir cmnist --save_dir corr --beta 0.1
3. Sample commands to run existing regularization/robustness methods:
- Maximum Likelihood Estimate (MLE):
python existing_methods.py --dataset fgbg_cmnist_cpr0.5-0.5 --seed 0 --root_dir cmnist --lr 0.0001 --bs 128 --save_dir mle
- Adaptive Batch Normalization (AdaBN):
python existing_methods.py --dataset fgbg_cmnist_cpr0.5-0.5 --seed 0 --root_dir cmnist --lr 0.0001 --bs 32 --save_dir adabn --bn --bn_eval
- Adversarial Logit Pairing (ALP):
python existing_methods.py --dataset fgbg_cmnist_cpr0.5-0.5 --seed 0 --root_dir cmnist --lr 0.0001 --save_dir alp --alp --nsteps 20 --stepsz 2 --epsilon 8 --beta 0.1
- Clean Logit Pairing (CLP):
python existing_methods.py --dataset fgbg_cmnist_cpr0.5-0.5 --seed 0 --root_dir cmnist --lr 0.0001 --save_dir clp --clp --beta 0.5
- Projected Gradient Descent (PGD) based adversarial training:
python existing_methods.py --dataset fgbg_cmnist_cpr0.5-0.5 --seed 0 --root_dir cmnist --lr 0.0001 --save_dir pgd --pgd --nsteps 20 --stepsz 2 --epsilon 8
- Variational Information Bottleneck (VIB):
python existing_methods.py --dataset fgbg_cmnist_cpr0.5-0.5 --seed 0 --root_dir cmnist --lr 0.001 --save_dir inp --inp_noise 0.2
- Input Noise:
python existing_methods.py --dataset fgbg_cmnist_cpr0.5-0.5 --seed 0 --root_dir cmnist --lr 0.001 --save_dir inp_noise --inp_noise 0.2
4. Evaluate a trained model on another dataset (here [root_dir] and [save_dir] should be the directories in which the model to be used is saved):
python eval.py --root_dir cmnist --save_dir corr --dataset mnistm