Projects
Reading
People
Chat
SU\G
(𝔸)
/K·U
Projects
Reading
People
Chat
Sign Up
Light
Dark
System
A simple way to make neural networks robust against diverse image corruptions
Evgenia Rusak
,
Lukas Schott
,
R. Zimmermann
,
Julian Bitterwolf
,
Oliver Bringmann
,
Matthias Bethge
,
Wieland Brendel
Type:
Preprint
Publication Date:
2020-01-16
Citations:
31
View
Share
Locations
arXiv (Cornell University) -
View
Similar Works
Action
Title
Year
Authors
+
A simple way to make neural networks robust against diverse image corruptions
2020
Evgenia Rusak
Lukas Schott
R. Zimmermann
Julian Bitterwolf
Oliver Bringmann
Matthias Bethge
Wieland Brendel
+
Increasing the robustness of DNNs against image corruptions by playing the Game of Noise
2020
Evgenia Rusak
Lukas Schott
R. Zimmermann
Julian Bitterwolf
Oliver Bringmann
Matthias Bethge
Wieland Brendel
+
Defending Against Image Corruptions Through Adversarial Augmentations
2021
Dan A. Calian
Florian Stimberg
Olivia Wiles
Sylvestre-Alvise Rebuffi
András György
Timothy Mann
Sven Gowal
+
Defending Against Image Corruptions Through Adversarial Augmentations
2021
Dan A. Calian
Florian Stimberg
Olivia Wiles
Sylvestre-Alvise Rebuffi
András György
Timothy Mann
Sven Gowal
+
PDF
Chat
A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions
2020
Evgenia Rusak
Lukas Schott
R. Zimmermann
Julian Bitterwolf
Oliver Bringmann
Matthias Bethge
Wieland Brendel
+
Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations
2018
Dan Hendrycks
Thomas G. Dietterich
+
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense
2023
Zunzhi You
Daochang Liu
Chang Xu
+
PRIME: A few primitives can boost robustness to common corruptions
2021
Apostolos Modas
Rahul Rade
Guillermo Ortiz-Jiménez
Seyed-Mohsen Moosavi-Dezfooli
Pascal Frossard
+
MNIST-C: A Robustness Benchmark for Computer Vision
2019
Norman Mu
Justin Gilmer
+
Improving robustness against common corruptions by covariate shift adaptation
2020
Steffen Schneider
Evgenia Rusak
Luisa Eck
Oliver Bringmann
Wieland Brendel
Matthias Bethge
+
Frequency-Based Vulnerability Analysis of Deep Learning Models against Image Corruptions
2023
Harshitha Machiraju
Michael H. Herzog
Pascal Frossard
+
PDF
Chat
Improving robustness against common corruptions with frequency biased models
2021
Tonmoy Saikia
Cordelia Schmid
Thomas Brox
+
On the effectiveness of adversarial training against common corruptions
2021
Klim Kireev
Maksym Andriushchenko
Nicolas Flammarion
+
On the effectiveness of adversarial training against common corruptions.
2021
Klim Kireev
Maksym Andriushchenko
Nicolas Flammarion
+
Improving robustness against common corruptions with frequency biased models
2021
Tonmoy Saikia
Cordelia Schmid
Thomas Brox
+
Improving robustness against common corruptions with frequency biased models
2021
Tonmoy Saikia
Cordelia Schmid
Thomas Brox
+
PDF
Chat
Improving robustness to corruptions with multiplicative weight perturbations
2024
Trung Trinh
Markus Heinonen
Luigi Acerbi
Samuel Kaski
+
PDF
Chat
Diverse Gaussian Noise Consistency Regularization for Robustness and Uncertainty Calibration
2023
Theodoros Tsiligkaridis
Athanasios Tsiligkaridis
+
PDF
Chat
Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond
2022
Yi Yu
Wenhan Yang
Yap‐Peng Tan
Alex C. Kot
+
Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond
2022
Yi Yu
Wenhan Yang
Yap‐Peng Tan
Alex C. Kot
Cited by (28)
Action
Title
Year
Authors
+
PDF
Chat
Multimodal Co-learning: Challenges, applications with datasets, recent advances and future directions
2021
Anil Rahate
Rahee Walambe
Sheela Ramanna
Ketan Kotecha
+
Learning perturbation sets for robust machine learning
2020
Eric Wong
J. Zico Kolter
+
Prepare for the Worst: Generalizing across Domain Shifts with Adversarial Batch Normalization
2021
Manli Shu
Zuxuan Wu
Micah Goldblum
Tom Goldstein
+
Untapped Potential of Data Augmentation: A Domain Generalization Viewpoint
2020
Vihari Piratla
Shiv Shankar
+
Improved Handling of Motion Blur in Online Object Detection
2020
Mohamed Sayed
Gabriel Brostow
+
A Closer Look at the Robustness of Vision-and-Language Pre-trained Models
2020
Linjie Li
Zhe Gan
Jingjing Liu
+
PDF
Chat
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
2021
Dan Hendrycks
Steven Basart
Norman Mu
Saurav Kadavath
Fengqiu Wang
Evan Dorundo
Rahul Desai
Tyler Zhu
Samyak Parajuli
Mike Guo
+
PDF
Chat
Adversarial momentum-contrastive pre-training
2022
Cong Xu
Dan Li
Min Yang
+
Bio-inspired Robustness: A Review
2021
Harshitha Machiraju
Oh-Hyeon Choung
Pascal Frossard
Michael H. Herzog
+
PDF
Chat
Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries
2021
Kun-Peng Ning
Lue Tao
Songcan Chen
Sheng-Jun Huang
+
Utilizing a null class to restrict decision spaces and defend against neural network adversarial attacks
2020
Matthew J. Roos
+
Using Learning Dynamics to Explore the Role of Implicit Regularization in Adversarial Examples
2020
Josue Ortega
Yilong Ju
Ryan Pyle
Ankit Patel
+
PDF
Chat
CrossNorm and SelfNorm for Generalization under Distribution Shifts
2021
Zhiqiang Tang
Yunhe Gao
Yi Zhu
Zhi Zhang
Mu Li
Dimitris Metaxas
+
Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation
2021
Chaithanya Kumar Mummadi
Robin Hutmacher
Kilian Rambach
Evgeny Levinkov
Thomas Brox
Jan Hendrik Metzen
+
Impact of Aliasing on Generalization in Deep Convolutional Networks
2021
Cristina Nader Vasconcelos
Hugo Larochelle
Vincent Dumoulin
Rob Romijnders
Nicolas Le Roux
Ross Goroshin
+
SelfNorm and CrossNorm for Out-of-Distribution Robustness
2021
Zhiqiang Tang
Yunhe Gao
Yi Zhu
Zhi Zhang
Mu Li
Dimitris Metaxas
+
Rethinking the Design Principles of Robust Vision Transformer
2021
Xiaofeng Mao
Gege Qi
Yuefeng Chen
Xiaodan Li
Shaokai Ye
Yuan He
Hui Xue
+
An Effective Anti-Aliasing Approach for Residual Networks
2020
Cristina Nader Vasconcelos
Hugo Larochelle
Vincent Dumoulin
Nicolas Le Roux
Ross Goroshin
+
Defending Against Image Corruptions Through Adversarial Augmentations
2021
Dan A. Calian
Florian Stimberg
Olivia Wiles
Sylvestre-Alvise Rebuffi
András György
Timothy Mann
Sven Gowal
+
Theoretical Study of Random Noise Defense against Query-Based Black-Box Attacks.
2021
Zeyu Qin
Yanbo Fan
Hongyuan Zha
Baoyuan Wu
+
Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs
2021
Avinash Baidya
Joel Dapello
James J. DiCarlo
Tiago Marques
+
Nuisance-Label Supervision: Robustness Improvement by Free Labels
2021
Xinyue Wei
Weichao Qiu
Yi Zhang
Zihao Xiao
Alan Yuille
+
PDF
Chat
Robust Image Classification Using a Low-Pass Activation Function and DCT Augmentation
2021
Md. Tahmid Hossain
Shyh Wei Teng
Ferdous Sohel
Guojun Lu
+
On the effectiveness of adversarial training against common corruptions.
2021
Klim Kireev
Maksym Andriushchenko
Nicolas Flammarion
+
Multispectral Object Detection with Deep Learning
2021
Osman Gani
Somenath Kuiry
Alaka Das
Mita Nasipuri
Nibaran Das
+
PDF
Chat
Deepfake Forensics via an Adversarial Game
2022
Wang Zhi
Yiwen Guo
Wangmeng Zuo
+
PDF
Chat
On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness
2021
Eric Mintun
Alexander Kirillov
Saining Xie
+
Improving robustness against common corruptions with frequency biased models
2021
Tonmoy Saikia
Cordelia Schmid
Thomas Brox
Citing (31)
Action
Title
Year
Authors
+
PDF
Chat
ImageNet Large Scale Visual Recognition Challenge
2015
Olga Russakovsky
Jia Deng
Hao Su
Jonathan Krause
Sanjeev Satheesh
Sean Ma
Zhiheng Huang
Andrej Karpathy
Aditya Khosla
Michael S. Bernstein
+
PDF
Chat
Deep Residual Learning for Image Recognition
2016
Kaiming He
Xiangyu Zhang
Shaoqing Ren
Jian Sun
+
Achieving Human Parity in Conversational Speech Recognition
2016
Wayne Xiong
Jasha Droppo
Xuedong Huang
Frank Seide
Mike Seltzer
Andreas Stolcke
Dong Yu
Geoffrey Zweig
+
PDF
Chat
Universal Adversarial Perturbations
2017
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
Omar Fawzi
Pascal Frossard
+
Quality Resilient Deep Neural Networks
2017
Samuel Dodge
Lina J. Karam
+
PDF
Chat
A Study and Comparison of Human and Deep Learning Recognition Performance under Visual Distortions
2017
Samuel Dodge
Lina J. Karam
+
Machine Learning as an Adversarial Service: Learning Black-Box Adversarial Examples.
2017
Jamie Hayes
George Danezis
+
A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations
2017
Logan Engstrom
Brandon Tran
Dimitris Tsipras
Ludwig Schmidt
Aleksander Mądry
+
AutoAugment: Learning Augmentation Policies from Data
2018
Ekin D. Cubuk
Barret Zoph
Dandelion Mané
Vijay Vasudevan
Quoc V. Le
+
Evaluating and Understanding the Robustness of Adversarial Logit Pairing.
2018
Logan Engstrom
Andrew Ilyas
Anish Athalye
+
Playing the Game of Universal Adversarial Perturbations
2018
Julien Pérolat
Mateusz Malinowski
Bilal Piot
Olivier Pietquin
+
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
2018
Robert Geirhos
Patricia Rubisch
Claudio Michaelis
Matthias Bethge
Felix A. Wichmann
Wieland Brendel
+
Quantifying Perceptual Distortion of Adversarial Examples
2019
Matt Jordan
Naren Sarayu Manoj
Surbhi Goel
Alexandros G. Dimakis
+
Transfer of Adversarial Robustness Between Perturbation Types
2019
Daniel Kang
Yi Sun
T. B. Brown
Dan Hendrycks
Jacob Steinhardt
+
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation
2019
Raphael Gontijo Lopes
Dong Yin
Ben Poole
Justin Gilmer
Ekin D. Cubuk
+
Adversarial Examples Are a Natural Consequence of Test Error in Noise
2019
Nic Ford
Justin Gilmer
Nicolas Carlini
Dogus Cubuk
+
Making Convolutional Networks Shift-Invariant Again
2019
Richard Zhang
+
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
2019
Claudio Michaelis
Benjamin Mitzkus
Robert Geirhos
Evgenia Rusak
Oliver Bringmann
Alexander S. Ecker
Matthias Bethge
Wieland Brendel
+
PDF
Chat
Feature Denoising for Improving Adversarial Robustness
2019
Cihang Xie
Yuxin Wu
Laurens van der Maaten
Alan Yuille
Kaiming He
+
Robustness of classifiers: from adversarial to random noise
2016
Alhussein Fawzi
Seyed-Mohsen Moosavi-Dezfooli
Pascal Frossard
+
PDF
Chat
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses
2019
Jérôme Rony
Luiz G. Hafemann
Luiz S. Oliveira
Ismail Ben Ayed
Robert Sabourin
Éric Granger
+
PDF
Chat
Exploring the Limits of Weakly Supervised Pretraining
2018
Dhruv Mahajan
Ross Girshick
Vignesh Ramanathan
Kaiming He
Manohar Paluri
Yixuan Li
Ashwin Bharambe
Laurens van der Maaten
+
PDF
Chat
Understanding how image quality affects deep neural networks
2016
Samuel Dodge
Lina J. Karam
+
Towards Deep Learning Models Resistant to Adversarial Attacks.
2018
Aleksander Mądry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
+
Adversarial training for free
2019
Ali Shafahi
Mahyar Najibi
Mohammad Ghiasi
Zheng Xu
John P. Dickerson
Christoph Studer
Larry S. Davis
Gavin Taylor
Tom Goldstein
+
A Fourier Perspective on Model Robustness in Computer Vision
2019
Dong Yin
Raphael Gontijo Lopes
Jonathon Shlens
Ekin D. Cubuk
Justin Gilmer
+
PDF
Chat
Defending Against Universal Perturbations With Shared Adversarial Training
2019
Chaithanya Kumar Mummadi
Thomas Brox
Jan Hendrik Metzen
+
PDF
Chat
Universal Adversarial Training
2020
Ali Shafahi
Mahyar Najibi
Zheng Xu
J.W.T. Dickerson
Larry S. Davis
Tom Goldstein
+
PDF
Chat
Self-Training With Noisy Student Improves ImageNet Classification
2020
Qizhe Xie
Minh-Thang Luong
Eduard Hovy
Quoc V. Le
+
Fast Differentiable Clipping-Aware Normalization and Rescaling
2020
Jonas Rauber
Matthias Bethge
+
PDF
Chat
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training
2019
Jan Laermann
Wojciech Samek
Nils Strodthoff