deepmind-research/adversarial_robustness/pytorch

PyTorch evaluation

We provide PyTorch evaluation code for convenience. If you developed a version of our training pipeline for PyTorch, please let us know as we will link it from here.

Here are known PyTorch implementations of our training pipeline:

• https://github.com/imrahulr/adversarial_robustness_pytorch (by Rahul Rade)

Here are few consideration when reproducing our training pipeline in PyTorch. As opposed to the RST code (provided by Carmon et al.):

• We set the batch normalization decay to 0.99 (instead of 0.9).
• We do not apply weight decay (l2 regularization) to the batch normalization scale and offset
• We use Haiku's default initialization for all layers (except the last, which is initialized with zeros).
• The PGD attack used during training uniformly initializes the initial solution over the l-p norm ball.
• We run the attack over the local batch statistics (rather than the evaluation statistics).
• We update batch normalization statistics from adversarial examples only ( rather than both clean and adversarial examples).
• We use 10 epochs warm-up to our learning schedule.