What it Thinks is Important is Important: Robustness Transfers through
Input Gradients

What it Thinks is Important is Important: Robustness Transfers through Input Gradients

11 December 2019

Papers citing "What it Thinks is Important is Important: Robustness Transfers through Input Gradients"

15 / 15 papers shown

Title
Indirect Gradient Matching for Adversarial Robust Distillation Hongsin Lee Seungju Cho Changick Kim AAML FedML 53 2 0 06 Dec 2023
$Releasing Inequality Phenomena in $L_{\infty}$-Adversarial Training via Input Gradient Distillation$ Releasing Inequality Phenomena in $L_{\infty}$ -Adversarial Training via Input Gradient Distillation Junxi Chen Junhao Dong Xiaohua Xie AAML 20 0 0 16 May 2023
Understanding Zero-Shot Adversarial Robustness for Large-Scale Models Chengzhi Mao Scott Geng Junfeng Yang Xin Eric Wang Carl Vondrick VLM 44 59 0 14 Dec 2022
Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning Xiaoyue Duan Guoliang Kang Runqi Wang Shumin Han Shenjun Xue Tian Wang Baochang Zhang 29 2 0 28 Nov 2022
Accelerating Certified Robustness Training via Knowledge Transfer Pratik Vaishnavi Kevin Eykholt Amir Rahmati 24 7 0 25 Oct 2022
Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training Saehyung Lee Hyungyu Lee AAML 29 2 0 27 Sep 2022
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning Tianlong Chen Sijia Liu Shiyu Chang Lisa Amini Zhangyang Wang CLL 26 4 0 15 Jun 2022
How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations? Alvin Chan Yew-Soon Ong Clement Tan AAML 24 13 0 09 May 2022
Does Robustness on ImageNet Transfer to Downstream Tasks? Yutaro Yamada Mayu Otani OOD 32 27 0 08 Apr 2022
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps Muhammad Awais Fengwei Zhou Chuanlong Xie Jiawei Li Sung-Ho Bae Zhenguo Li AAML 40 17 0 09 Nov 2021
Simple Post-Training Robustness Using Test Time Augmentations and Random Forest Gilad Cohen Raja Giryes AAML 35 4 0 16 Sep 2021
Adversarial Robustness for Unsupervised Domain Adaptation Muhammad Awais Fengwei Zhou Hang Xu Lanqing Hong Ping Luo Sung-Ho Bae Zhenguo Li 20 39 0 02 Sep 2021
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning Ren Wang Kaidi Xu Sijia Liu Pin-Yu Chen Tsui-Wei Weng Chuang Gan Meng Wang AAML 21 46 0 20 Feb 2021
KeepAugment: A Simple Information-Preserving Data Augmentation Approach Chengyue Gong Dilin Wang Meng Li Vikas Chandra Qiang Liu 33 113 0 23 Nov 2020
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 29 48 0 19 Oct 2020