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