Implementation of SSR and Uniform Gradient
License: MIT License
See the paper on arxiv
P.S.: We are working on Tensorflow 2 version for the future release. Check back later.
Use Keras
cd eval/keras_v1
python run_eval.py --attr_fn saliency --epsilon 2.0 --max_iters 50 --k_top 20 --pixel_max 1 --weight_file weight/pgd_5.h5 --attack_method topK --measure intersection --saving_prefix result/sweep_pgd/test_report_topK_05_
Use Pytorch
see notebooks under eval/pytorch
cd example
python train_smooth_cifar.py --alpha 0.3
Thank you for uploading great code. and I have a question regarding BetaLoss.
In line 22, I think B_T and B should be swapped.
What I understand is that we want to calculate W(diag(p)-p^Tp)W^T to approximate H, and then we find a maximum eigenvalue for the approximated H.
In smooth_space.py, variable 'A' looks diag(p)-p^Tp, and following e and U are eigenvalue and eigenvector of A, respectively.
Since B = WUS, BB^T gives W(U S S^T U^T) W^T = W(US^2U^T) W^T = WAW^T = H. (Note S = Sigma in code)
However, in line 22, this code calculates B^TB, not BB^T
Could you tell me what I missing?
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