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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
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Papers citing
"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness"
39 / 1,489 papers shown
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IEEE Access (IEEE Access), 2019
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31 May 2019
Learning Robust Global Representations by Penalizing Local Predictive Power
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High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks
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28 May 2019
Cross-Domain Transferability of Adversarial Perturbations
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Muzammal Naseer
Salman H. Khan
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Fatih Porikli
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494
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28 May 2019
Provable robustness against all adversarial
l
p
l_p
l
p
-perturbations for
p
≥
1
p\geq 1
p
≥
1
International Conference on Learning Representations (ICLR), 2019
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Matthias Hein
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27 May 2019
Robust Classification using Robust Feature Augmentation
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25 May 2019
Interpreting Adversarially Trained Convolutional Neural Networks
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23 May 2019
Zero-shot Knowledge Transfer via Adversarial Belief Matching
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Exploring Structural Sparsity of Deep Networks via Inverse Scale Spaces
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10 May 2019
Adversarial Examples Are Not Bugs, They Are Features
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30 Apr 2019
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208
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Unrestricted Adversarial Examples via Semantic Manipulation
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174
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12 Apr 2019
An Analysis of Pre-Training on Object Detection
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Bharat Singh
Mahyar Najibi
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11 Apr 2019
Towards Analyzing Semantic Robustness of Deep Neural Networks
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277
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Person Re-identification with Bias-controlled Adversarial Training
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30 Mar 2019
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
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Nic Ford
Justin Gilmer
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Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks
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T. Breckon
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F. Koushanfar
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238
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08 Sep 2017
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