no code implementations • 30 Nov 2023 • Avery Ma, Amir-Massoud Farahmand, Yangchen Pan, Philip Torr, Jindong Gu
During the alignment process, the parameters of the source model are fine-tuned to minimize an alignment loss.
1 code implementation • 26 Oct 2023 • Jindong Gu, Xiaojun Jia, Pau de Jorge, Wenqain Yu, Xinwei Liu, Avery Ma, Yuan Xun, Anjun Hu, Ashkan Khakzar, Zhijiang Li, Xiaochun Cao, Philip Torr
This survey explores the landscape of the adversarial transferability of adversarial examples.
1 code implementation • 13 Aug 2023 • Avery Ma, Yangchen Pan, Amir-Massoud Farahmand
In the context of deep learning, our experiments show that SGD-trained neural networks have smaller Lipschitz constants, explaining the better robustness to input perturbations than those trained with adaptive gradient methods.
1 code implementation • 31 Oct 2022 • Avery Ma, Nikita Dvornik, Ran Zhang, Leila Pishdad, Konstantinos G. Derpanis, Afsaneh Fazly
For image classification, the most popular data augmentation techniques range from simple photometric and geometrical transformations, to more complex methods that use visual saliency to craft new training examples.
no code implementations • 17 Feb 2021 • Avery Ma, Aladin Virmaux, Kevin Scaman, Juwei Lu
Do all adversarial examples have the same consequences?
no code implementations • 4 Apr 2020 • Avery Ma, Fartash Faghri, Nicolas Papernot, Amir-Massoud Farahmand
Adversarial training is a common approach to improving the robustness of deep neural networks against adversarial examples.