LRNAS: Differentiable Searching for Adversarially Robust Lightweight Neural Architecture

This is the code implementation for the paper "LRNAS: Differentiable Searching for Adversarially Robust Lightweight Neural Architecture".

Requirements

• Python 3.7.4
• torch 1.8.0 + cu111
• torchvision 0.9.0 + cu111
• torchattacks 3.5.1

Run

• Run train_search.py to perform the search process to search for the CNN architectures.
• Run adv_train.py to train the searched architectures on CIFAR-10 or CIFAR-100.
• Run adv_train_tinyimagenet.py or adv_train_imagenet.py to train architectures on Tiny-ImageNet-200 or ImageNet-1K, respectively.
• Run adv_test.py to test the trained architectures on CIFAR-10, CIFAR-100, or Tiny-ImageNet-200.
• Run adv_test_imagenet.py to test the trained architectures on ImageNet-1K.

References

• DARTS
• AdvRush
• torchattacks
• Fast Adversarial Training