corr_based_prediction

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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