Скачать с ютуб Gradient with respect to input in PyTorch (FGSM attack + Integrated Gradients) в хорошем качестве

Gradient with respect to input in PyTorch (FGSM attack + Integrated Gradients)

In this video, I describe what the gradient with respect to input is. I also implement two specific examples of how one can use it: Fast gradient sign method (FGSM) and Integrated gradients. The first one is an adversarial attack that perturbs the input image in a way that fools a classifier network. The second is an approach that tries to interpret what features the classifier network is using to make a given prediction. Code: https://github.com/jankrepl/mildlyove... 00:00​​​ Intro 00:45​​​ FGSM method paper 01:56​​ Utility functions 07:22 FGSM example 12:58​​​ FGSM result 13:23​​​ Integrated gradients paper 14:32 Integrated gradients example 18:37 Integrated gradients result 19:13​ Cool projects + outro Explaining and Harnessing Adversarial Examples - https://arxiv.org/abs/1412.6572 Axiomatic Attribution for Deep Networks - https://arxiv.org/abs/1703.01365 Foolbox: https://github.com/bethgelab/foolbox Captum: https://github.com/pytorch/captum If you have any video suggestions or you just wanna chat feel free to join the discord server:   / discord   Credits logo animation Title: Conjungation · Author: Uncle Milk · Source:   / unclemilk​   · License: https://creativecommons.org/licenses/... · Download (9MB): https://auboutdufil.com/?id=600