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ImageNet-trained CNNs are biased towards texture; increasing shape bias
  improves accuracy and robustness
v1v2v3 (latest)

ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

29 November 2018
Robert Geirhos
Patricia Rubisch
Claudio Michaelis
Matthias Bethge
Felix Wichmann
Wieland Brendel
ArXiv (abs)PDFHTML

Papers citing "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness"

50 / 1,489 papers shown
Bias-based Universal Adversarial Patch Attack for Automatic Check-out
Bias-based Universal Adversarial Patch Attack for Automatic Check-out
Aishan Liu
Jinyang Guo
Xianglong Liu
Bowen Cao
Chongzhi Zhang
Hang Yu
AAML
262
6
0
19 May 2020
Spatiotemporal Attacks for Embodied Agents
Spatiotemporal Attacks for Embodied Agents
Aishan Liu
Tairan Huang
Xianglong Liu
Yitao Xu
Yuqing Ma
Xinyun Chen
Stephen J. Maybank
Dacheng Tao
AAML
147
0
0
19 May 2020
Enhancing Perceptual Loss with Adversarial Feature Matching for
  Super-Resolution
Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution
Ravi Tej Akella
Shirsendu Sukanta Halder
Arunav Shandeelya
Vinod Pankajakshan
GANSupR
103
16
0
15 May 2020
Reference-Based Sketch Image Colorization using Augmented-Self Reference
  and Dense Semantic Correspondence
Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence
Junsoo Lee
Eungyeup Kim
Yunsung Lee
Dongjun Kim
Jaehyuk Chang
Jaegul Choo
DiffM
302
173
0
11 May 2020
Multi-Task Learning in Histo-pathology for Widely Generalizable Model
Multi-Task Learning in Histo-pathology for Widely Generalizable Model
Jevgenij Gamper
Navid Alemi Koohbanani
Nasir M. Rajpoot
148
7
0
09 May 2020
How Can CNNs Use Image Position for Segmentation?
How Can CNNs Use Image Position for Segmentation?
Rito Murase
Masanori Suganuma
Takayuki Okatani
SSeg
142
14
0
07 May 2020
Towards explainable classifiers using the counterfactual approach --
  global explanations for discovering bias in data
Towards explainable classifiers using the counterfactual approach -- global explanations for discovering bias in data
Agnieszka Mikołajczyk
M. Grochowski
Arkadiusz Kwasigroch
FAttCML
130
4
0
05 May 2020
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and
  Information Security
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security
Michael R. Smith
Nicholas T. Johnson
J. Ingram
A. Carbajal
Ramyaa
Evelyn Domschot
Christopher C. Lamb
Stephen J Verzi
W. Kegelmeyer
AAML
214
5
0
04 May 2020
Video Contents Understanding using Deep Neural Networks
Video Contents Understanding using Deep Neural Networks
Mohammadhossein Toutiaee
Abbas Keshavarzi
Abolfazl Farahani
J. Miller
106
7
0
29 Apr 2020
Style-transfer GANs for bridging the domain gap in synthetic pose
  estimator training
Style-transfer GANs for bridging the domain gap in synthetic pose estimator trainingInternational Conference on Artificial Intelligence and Virtual Reality (AIVR), 2020
Pavel Rojtberg
Thomas Pollabauer
Arjan Kuijper
GAN
141
19
0
28 Apr 2020
Psychophysical Evaluation of Deep Re-Identification Models
Psychophysical Evaluation of Deep Re-Identification Models
Hamish Nicholson
170
1
0
28 Apr 2020
Mining self-similarity: Label super-resolution with epitomic
  representations
Mining self-similarity: Label super-resolution with epitomic representations
Nikolay Malkin
Anthony Ortiz
Caleb Robinson
Nebojsa Jojic
182
12
0
24 Apr 2020
YOLOv4: Optimal Speed and Accuracy of Object Detection
YOLOv4: Optimal Speed and Accuracy of Object Detection
Alexey Bochkovskiy
Chien-Yao Wang
H. Liao
VLMObjD
419
14,639
0
23 Apr 2020
Five Points to Check when Comparing Visual Perception in Humans and
  Machines
Five Points to Check when Comparing Visual Perception in Humans and Machines
Christina M. Funke
Judy Borowski
Karolina Stosio
Wieland Brendel
Thomas S. A. Wallis
Matthias Bethge
225
34
0
20 Apr 2020
Shortcut Learning in Deep Neural Networks
Shortcut Learning in Deep Neural NetworksNature Machine Intelligence (NMI), 2020
Robert Geirhos
J. Jacobsen
Claudio Michaelis
R. Zemel
Wieland Brendel
Matthias Bethge
Felix Wichmann
1.1K
2,475
0
16 Apr 2020
Top-Down Networks: A coarse-to-fine reimagination of CNNs
Top-Down Networks: A coarse-to-fine reimagination of CNNs
Ioannis Lelekas
Nergis Tomen
S. Pintea
Jan van Gemert
99
6
0
16 Apr 2020
WQT and DG-YOLO: towards domain generalization in underwater object
  detection
WQT and DG-YOLO: towards domain generalization in underwater object detection
Hong Liu
Pinhao Song
Runwei Ding
126
30
0
14 Apr 2020
PatchAttack: A Black-box Texture-based Attack with Reinforcement
  Learning
PatchAttack: A Black-box Texture-based Attack with Reinforcement LearningEuropean Conference on Computer Vision (ECCV), 2020
Chenglin Yang
Adam Kortylewski
Cihang Xie
Yinzhi Cao
Alan Yuille
AAML
251
128
0
12 Apr 2020
Exploring The Spatial Reasoning Ability of Neural Models in Human IQ
  Tests
Exploring The Spatial Reasoning Ability of Neural Models in Human IQ TestsNeural Networks (NN), 2020
Hyunjae Kim
Yookyung Koh
Jinheon Baek
Jaewoo Kang
117
5
0
11 Apr 2020
Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics
Steering Self-Supervised Feature Learning Beyond Local Pixel StatisticsComputer Vision and Pattern Recognition (CVPR), 2020
Simon Jenni
Hailin Jin
Paolo Favaro
SSL
176
50
0
05 Apr 2020
Approximate Manifold Defense Against Multiple Adversarial Perturbations
Approximate Manifold Defense Against Multiple Adversarial PerturbationsIEEE International Joint Conference on Neural Network (IJCNN), 2020
Jay Nandy
Wynne Hsu
Yang Deng
AAML
187
12
0
05 Apr 2020
Multimodal Material Classification for Robots using Spectroscopy and
  High Resolution Texture Imaging
Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture ImagingIEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2020
Zackory M. Erickson
Eliot Xing
Bharat Srirangam
Sonia Chernova
Charles C. Kemp
255
45
0
02 Apr 2020
Bias in Machine Learning -- What is it Good for?
Bias in Machine Learning -- What is it Good for?
Thomas Hellström
Virginia Dignum
Suna Bensch
AI4CEFaML
127
3
0
01 Apr 2020
Improving out-of-distribution generalization via multi-task
  self-supervised pretraining
Improving out-of-distribution generalization via multi-task self-supervised pretraining
Isabela Albuquerque
Nikhil Naik
Junnan Li
N. Keskar
R. Socher
SSLOOD
188
44
0
30 Mar 2020
Can you hear me $\textit{now}$? Sensitive comparisons of human and
  machine perception
Can you hear me now\textit{now}now? Sensitive comparisons of human and machine perceptionCognitive Sciences (CogSci), 2020
Michael A. Lepori
C. Firestone
AAML
233
10
0
27 Mar 2020
Going in circles is the way forward: the role of recurrence in visual
  inference
Going in circles is the way forward: the role of recurrence in visual inferenceCurrent Opinion in Neurobiology (Curr Opin Neurobiol), 2020
R. S. V. Bergen
N. Kriegeskorte
322
91
0
26 Mar 2020
Understanding the robustness of deep neural network classifiers for
  breast cancer screening
Understanding the robustness of deep neural network classifiers for breast cancer screening
Witold Oleszkiewicz
Taro Makino
Stanislaw Jastrzebski
Tomasz Trzciñski
Linda Moy
Dong Wang
Laura Heacock
Krzysztof J. Geras
135
1
0
23 Mar 2020
Overinterpretation reveals image classification model pathologies
Overinterpretation reveals image classification model pathologiesNeural Information Processing Systems (NeurIPS), 2020
Brandon Carter
Siddhartha Jain
Jonas W. Mueller
David K Gifford
FAtt
286
55
0
19 Mar 2020
Learning to Structure an Image with Few Colors
Learning to Structure an Image with Few ColorsComputer Vision and Pattern Recognition (CVPR), 2020
Yunzhong Hou
Liang Zheng
Stephen Gould
MQ
266
23
0
17 Mar 2020
Synthesizing human-like sketches from natural images using a conditional
  convolutional decoder
Synthesizing human-like sketches from natural images using a conditional convolutional decoderIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2020
Moritz Kampelmühler
A. Pinz
163
20
0
16 Mar 2020
On the Texture Bias for Few-Shot CNN Segmentation
On the Texture Bias for Few-Shot CNN SegmentationIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2020
Reza Azad
A. Fayjie
C. Kauffmann
Ismail Ben Ayed
M. Pedersoli
Jose Dolz
SSeg
289
74
0
09 Mar 2020
An Empirical Evaluation on Robustness and Uncertainty of Regularization
  Methods
An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods
Sanghyuk Chun
Seong Joon Oh
Sangdoo Yun
Dongyoon Han
Junsuk Choe
Y. Yoo
AAMLOOD
681
55
0
09 Mar 2020
Salient Facial Features from Humans and Deep Neural Networks
Salient Facial Features from Humans and Deep Neural Networks
Shan Sun
Wei Zhen Teoh
Michael Guerzhoy
3DHCVBMFAtt
77
0
0
08 Mar 2020
Curriculum By Smoothing
Curriculum By Smoothing
Samarth Sinha
Animesh Garg
Hugo Larochelle
369
7
0
03 Mar 2020
Learning Texture Invariant Representation for Domain Adaptation of
  Semantic Segmentation
Learning Texture Invariant Representation for Domain Adaptation of Semantic SegmentationComputer Vision and Pattern Recognition (CVPR), 2020
Myeongjin Kim
H. Byun
OOD
274
301
0
02 Mar 2020
Out-of-Distribution Generalization via Risk Extrapolation (REx)
Out-of-Distribution Generalization via Risk Extrapolation (REx)International Conference on Machine Learning (ICML), 2020
David M. Krueger
Ethan Caballero
J. Jacobsen
Amy Zhang
Jonathan Binas
Dinghuai Zhang
Rémi Le Priol
Aaron Courville
OOD
951
1,111
0
02 Mar 2020
Learning Cross-domain Generalizable Features by Representation
  Disentanglement
Learning Cross-domain Generalizable Features by Representation Disentanglement
Qingjie Meng
Daniel Rueckert
Bernhard Kainz
OODDRL
178
12
0
29 Feb 2020
Exploring and Distilling Cross-Modal Information for Image Captioning
Exploring and Distilling Cross-Modal Information for Image CaptioningInternational Joint Conference on Artificial Intelligence (IJCAI), 2019
Fenglin Liu
Xuancheng Ren
Yuanxin Liu
Kai Lei
Xu Sun
ViT
195
55
0
28 Feb 2020
A Novel Measure to Evaluate Generative Adversarial Networks Based on
  Direct Analysis of Generated Images
A Novel Measure to Evaluate Generative Adversarial Networks Based on Direct Analysis of Generated Images
Shuyue Guan
Murray H. Loew
EGVM
202
13
0
27 Feb 2020
Unbiased Scene Graph Generation from Biased Training
Unbiased Scene Graph Generation from Biased TrainingComputer Vision and Pattern Recognition (CVPR), 2020
Kaihua Tang
Yulei Niu
Jianqiang Huang
Jiaxin Shi
Hanwang Zhang
CML
473
781
0
27 Feb 2020
CheXpedition: Investigating Generalization Challenges for Translation of
  Chest X-Ray Algorithms to the Clinical Setting
CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting
Pranav Rajpurkar
Anirudh Joshi
Anuj Pareek
Phil Chen
Amirhossein Kiani
Jeremy Irvin
A. Ng
M. Lungren
LM&MA
168
49
0
26 Feb 2020
On Feature Normalization and Data Augmentation
On Feature Normalization and Data AugmentationComputer Vision and Pattern Recognition (CVPR), 2020
Boyi Li
Felix Wu
Ser-Nam Lim
Serge J. Belongie
Kilian Q. Weinberger
247
156
0
25 Feb 2020
Searching for Winograd-aware Quantized Networks
Searching for Winograd-aware Quantized NetworksConference on Machine Learning and Systems (MLSys), 2020
Javier Fernandez-Marques
P. Whatmough
Andrew Mundy
Matthew Mattina
MQ
144
41
0
25 Feb 2020
I Am Going MAD: Maximum Discrepancy Competition for Comparing
  Classifiers Adaptively
I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers AdaptivelyInternational Conference on Learning Representations (ICLR), 2020
Haotao Wang
Tianlong Chen
Zinan Lin
Kede Ma
VLM
206
20
0
25 Feb 2020
Adversarial Perturbations Prevail in the Y-Channel of the YCbCr Color
  Space
Adversarial Perturbations Prevail in the Y-Channel of the YCbCr Color Space
Camilo Pestana
Naveed Akhtar
Wei Liu
D. Glance
Lin Wang
AAML
146
10
0
25 Feb 2020
Utilizing a null class to restrict decision spaces and defend against
  neural network adversarial attacks
Utilizing a null class to restrict decision spaces and defend against neural network adversarial attacks
Matthew J. Roos
AAML
67
2
0
24 Feb 2020
Exploiting the Full Capacity of Deep Neural Networks while Avoiding
  Overfitting by Targeted Sparsity Regularization
Exploiting the Full Capacity of Deep Neural Networks while Avoiding Overfitting by Targeted Sparsity Regularization
Karim Huesmann
Soeren Klemm
Lars Linsen
Benjamin Risse
115
2
0
21 Feb 2020
Automatic Shortcut Removal for Self-Supervised Representation Learning
Automatic Shortcut Removal for Self-Supervised Representation LearningInternational Conference on Machine Learning (ICML), 2020
Matthias Minderer
Olivier Bachem
N. Houlsby
Michael Tschannen
SSL
272
77
0
20 Feb 2020
Recurrent Attention Model with Log-Polar Mapping is Robust against
  Adversarial Attacks
Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks
Taro Kiritani
Koji Ono
AAML
135
3
0
13 Feb 2020
Edge-Gated CNNs for Volumetric Semantic Segmentation of Medical Images
Edge-Gated CNNs for Volumetric Semantic Segmentation of Medical ImagesbioRxiv (bioRxiv), 2020
Ali Hatamizadeh
Demetri Terzopoulos
Andriy Myronenko
147
18
0
11 Feb 2020
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