ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2004.11273
  4. Cited By
Ensemble Generative Cleaning with Feedback Loops for Defending
  Adversarial Attacks

Ensemble Generative Cleaning with Feedback Loops for Defending Adversarial Attacks

23 April 2020
Jianhe Yuan
Zhihai He
    AAML
ArXiv (abs)PDFHTML

Papers citing "Ensemble Generative Cleaning with Feedback Loops for Defending Adversarial Attacks"

10 / 10 papers shown
VQUNet: Vector Quantization U-Net for Defending Adversarial Atacks by
  Regularizing Unwanted Noise
VQUNet: Vector Quantization U-Net for Defending Adversarial Atacks by Regularizing Unwanted Noise
Zhixun He
Mukesh Singhal
273
3
0
05 Jun 2024
LRR: Language-Driven Resamplable Continuous Representation against
  Adversarial Tracking Attacks
LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking AttacksInternational Conference on Learning Representations (ICLR), 2024
Jianlang Chen
Xuhong Ren
Qing Guo
Felix Juefei Xu
Di Lin
Wei Feng
Lei Ma
Jianjun Zhao
273
6
0
09 Apr 2024
Robust Overfitting Does Matter: Test-Time Adversarial Purification With
  FGSM
Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSMComputer Vision and Pattern Recognition (CVPR), 2024
Linyu Tang
Lei Zhang
AAML
237
13
0
18 Mar 2024
Defense against Adversarial Cloud Attack on Remote Sensing Salient
  Object Detection
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object DetectionIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2023
Huiming Sun
Lan Fu
Jinlong Li
Qing Guo
Zibo Meng
Tianyun Zhang
Yuewei Lin
Hongkai Yu
AAML
275
18
0
30 Jun 2023
A Survey on the Robustness of Computer Vision Models against Common
  Corruptions
A Survey on the Robustness of Computer Vision Models against Common Corruptions
Shunxin Wang
Raymond N. J. Veldhuis
Christoph Brune
N. Strisciuglio
OODVLM
737
27
0
10 May 2023
DISCO: Adversarial Defense with Local Implicit Functions
DISCO: Adversarial Defense with Local Implicit FunctionsNeural Information Processing Systems (NeurIPS), 2022
Chih-Hui Ho
Nuno Vasconcelos
AAML
473
57
0
11 Dec 2022
An Eye for an Eye: Defending against Gradient-based Attacks with
  Gradients
An Eye for an Eye: Defending against Gradient-based Attacks with Gradients
Hanbin Hong
Yuan Hong
Yu Kong
AAML
289
3
0
02 Feb 2022
AGKD-BML: Defense Against Adversarial Attack by Attention Guided
  Knowledge Distillation and Bi-directional Metric Learning
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric LearningIEEE International Conference on Computer Vision (ICCV), 2021
Hong Wang
Yuefan Deng
Shinjae Yoo
Haibin Ling
Lu Ma
AAML
259
21
0
13 Aug 2021
Advances in adversarial attacks and defenses in computer vision: A
  survey
Advances in adversarial attacks and defenses in computer vision: A survey
Naveed Akhtar
Lin Wang
Navid Kardan
M. Shah
AAML
545
315
0
01 Aug 2021
Towards Robust Neural Networks via Orthogonal Diversity
Towards Robust Neural Networks via Orthogonal DiversityPattern Recognition (Pattern Recognit.), 2020
Kun Fang
Qinghua Tao
Yingwen Wu
Tao Li
Jia Cai
Feipeng Cai
Xiaolin Huang
Jie Yang
AAML
345
14
0
23 Oct 2020
1
Page 1 of 1