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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

About

This repo provides code used in the paper "Predicting with High Correlation Features" (https://arxiv.org/abs/1910.00164):

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