A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning

25 December 2020

Papers citing "A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning"

9 / 9 papers shown

Title
Guiding the retraining of convolutional neural networks against adversarial inputs Francisco Durán Silverio Martínez-Fernández Michael Felderer Xavier Franch AAML 35 1 0 08 Jul 2022
Hierarchical Distribution-Aware Testing of Deep Learning Wei Huang Xingyu Zhao Alec Banks V. Cox Xiaowei Huang OOD AAML 36 10 0 17 May 2022
Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4 William Berrios Arturo Deza MedIm ViT 30 13 0 08 Mar 2022
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks Salijona Dyrmishi Salah Ghamizi Thibault Simonetto Yves Le Traon Maxime Cordy AAML 26 16 0 07 Feb 2022
Improving Robustness by Enhancing Weak Subnets Yong Guo David Stutz Bernt Schiele AAML 27 15 0 30 Jan 2022
ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients Alessandro Cappelli Julien Launay Laurent Meunier Ruben Ohana Iacopo Poli AAML 24 4 0 06 Jul 2021
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 58 63 0 02 Mar 2020
Disentangling Adversarial Robustness and Generalization David Stutz Matthias Hein Bernt Schiele AAML OOD 194 273 0 03 Dec 2018
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 287 5,837 0 08 Jul 2016