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Supporting code for the paper: Weight-Covariance Alignment for Adversarially Robust Neural Networks. Eustratiadis et al. (ICML 2021)

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Name		Name	Last commit message	Last commit date
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attacks		attacks
config		config
.gitignore		.gitignore
README.md		README.md
data_loaders.py		data_loaders.py
metrics.py		metrics.py
models.py		models.py
requirements.txt		requirements.txt
resnet.py		resnet.py
run.py		run.py
test.py		test.py
train.py		train.py
utils.py		utils.py

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WCA-Net

How to train a new model


python run.py train <path to configuration file>

Configuration files

m0: Vanilla model (backbone + classification layer)
m1: Stochastic isotropic model, trained with WCA
m2: Stochastic anisotropic model, trained with WCA
m3: Stochastic anisotropic model, trained with WCA, adversarial training

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Supporting code for the paper: Weight-Covariance Alignment for Adversarially Robust Neural Networks. Eustratiadis et al. (ICML 2021)

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