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Geometric robustness of deep networks: analysis and improvement

Geometric robustness of deep networks: analysis and improvement

24 November 2017
Can Kanbak
Seyed-Mohsen Moosavi-Dezfooli
P. Frossard
    OOD
    AAML
ArXivPDFHTML

Papers citing "Geometric robustness of deep networks: analysis and improvement"

29 / 79 papers shown
Title
Efficient Certification of Spatial Robustness
Efficient Certification of Spatial Robustness
Anian Ruoss
Maximilian Baader
Mislav Balunović
Martin Vechev
AAML
13
25
0
19 Sep 2020
Model Patching: Closing the Subgroup Performance Gap with Data
  Augmentation
Model Patching: Closing the Subgroup Performance Gap with Data Augmentation
Karan Goel
Albert Gu
Yixuan Li
Christopher Ré
16
118
0
15 Aug 2020
Adversarial Examples on Object Recognition: A Comprehensive Survey
Adversarial Examples on Object Recognition: A Comprehensive Survey
A. Serban
E. Poll
Joost Visser
AAML
25
73
0
07 Aug 2020
Stronger and Faster Wasserstein Adversarial Attacks
Stronger and Faster Wasserstein Adversarial Attacks
Kaiwen Wu
Allen Wang
Yaoliang Yu
AAML
14
32
0
06 Aug 2020
Realistic Adversarial Data Augmentation for MR Image Segmentation
Realistic Adversarial Data Augmentation for MR Image Segmentation
Chia-Ju Chen
C. Qin
Huaqi Qiu
Cheng Ouyang
Shuo Wang
Liang Chen
G. Tarroni
Wenjia Bai
Daniel Rueckert
GAN
MedIm
25
82
0
23 Jun 2020
Model-Based Robust Deep Learning: Generalizing to Natural,
  Out-of-Distribution Data
Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data
Alexander Robey
Hamed Hassani
George J. Pappas
OOD
43
42
0
20 May 2020
Adversarial Training against Location-Optimized Adversarial Patches
Adversarial Training against Location-Optimized Adversarial Patches
Sukrut Rao
David Stutz
Bernt Schiele
AAML
19
91
0
05 May 2020
On Translation Invariance in CNNs: Convolutional Layers can Exploit
  Absolute Spatial Location
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
O. Kayhan
Jan van Gemert
209
232
0
16 Mar 2020
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve
  Adversarial Robustness
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness
Ahmadreza Jeddi
M. Shafiee
Michelle Karg
C. Scharfenberger
A. Wong
OOD
AAML
58
63
0
02 Mar 2020
Certified Defense to Image Transformations via Randomized Smoothing
Certified Defense to Image Transformations via Randomized Smoothing
Marc Fischer
Maximilian Baader
Martin Vechev
AAML
14
66
0
27 Feb 2020
Achieving Robustness in the Wild via Adversarial Mixing with
  Disentangled Representations
Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations
Sven Gowal
Chongli Qin
Po-Sen Huang
taylan. cemgil
Krishnamurthy Dvijotham
Timothy A. Mann
Pushmeet Kohli
AAML
OOD
18
57
0
06 Dec 2019
Active Subspace of Neural Networks: Structural Analysis and Universal
  Attacks
Active Subspace of Neural Networks: Structural Analysis and Universal Attacks
Chunfeng Cui
Kaiqi Zhang
Talgat Daulbaev
Julia Gusak
Ivan Oseledets
Zheng-Wei Zhang
AAML
29
25
0
29 Oct 2019
Sparse and Imperceivable Adversarial Attacks
Sparse and Imperceivable Adversarial Attacks
Francesco Croce
Matthias Hein
AAML
39
199
0
11 Sep 2019
Invariance-inducing regularization using worst-case transformations
  suffices to boost accuracy and spatial robustness
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness
Fanny Yang
Zuowen Wang
C. Heinze-Deml
28
42
0
26 Jun 2019
Do Image Classifiers Generalize Across Time?
Do Image Classifiers Generalize Across Time?
Vaishaal Shankar
Achal Dave
Rebecca Roelofs
Deva Ramanan
Benjamin Recht
Ludwig Schmidt
20
82
0
05 Jun 2019
On instabilities of deep learning in image reconstruction - Does AI come
  at a cost?
On instabilities of deep learning in image reconstruction - Does AI come at a cost?
Vegard Antun
F. Renna
C. Poon
Ben Adcock
A. Hansen
16
597
0
14 Feb 2019
Do ImageNet Classifiers Generalize to ImageNet?
Do ImageNet Classifiers Generalize to ImageNet?
Benjamin Recht
Rebecca Roelofs
Ludwig Schmidt
Vaishaal Shankar
OOD
SSeg
VLM
40
1,657
0
13 Feb 2019
Perception-in-the-Loop Adversarial Examples
Perception-in-the-Loop Adversarial Examples
Mahmoud Salamati
Sadegh Soudjani
R. Majumdar
AAML
13
2
0
21 Jan 2019
Data Augmentation with Manifold Exploring Geometric Transformations for
  Increased Performance and Robustness
Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness
Magdalini Paschali
Walter Simson
Abhijit Guha Roy
Muhammad Ferjad Naeem
Rudiger Gobl
Christian Wachinger
Nassir Navab
AAML
22
21
0
14 Jan 2019
Attacks on State-of-the-Art Face Recognition using Attentional
  Adversarial Attack Generative Network
Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network
Q. Song
Yingqi Wu
Lu Yang
AAML
CVBM
GAN
16
96
0
29 Nov 2018
SparseFool: a few pixels make a big difference
SparseFool: a few pixels make a big difference
Apostolos Modas
Seyed-Mohsen Moosavi-Dezfooli
P. Frossard
AAML
6
197
0
06 Nov 2018
Cost-Sensitive Robustness against Adversarial Examples
Cost-Sensitive Robustness against Adversarial Examples
Xiao Zhang
David E. Evans
AAML
23
25
0
22 Oct 2018
Adversarial Examples - A Complete Characterisation of the Phenomenon
Adversarial Examples - A Complete Characterisation of the Phenomenon
A. Serban
E. Poll
Joost Visser
SILM
AAML
27
49
0
02 Oct 2018
Pooling is neither necessary nor sufficient for appropriate deformation
  stability in CNNs
Pooling is neither necessary nor sufficient for appropriate deformation stability in CNNs
Avraham Ruderman
Neil C. Rabinowitz
Ari S. Morcos
Daniel Zoran
6
41
0
12 Apr 2018
On the Suitability of $L_p$-norms for Creating and Preventing
  Adversarial Examples
On the Suitability of LpL_pLp​-norms for Creating and Preventing Adversarial Examples
Mahmood Sharif
Lujo Bauer
Michael K. Reiter
AAML
16
137
0
27 Feb 2018
Divide, Denoise, and Defend against Adversarial Attacks
Divide, Denoise, and Defend against Adversarial Attacks
Seyed-Mohsen Moosavi-Dezfooli
A. Shrivastava
Oncel Tuzel
AAML
24
45
0
19 Feb 2018
Robustness of Rotation-Equivariant Networks to Adversarial Perturbations
Robustness of Rotation-Equivariant Networks to Adversarial Perturbations
Beranger Dumont
Simona Maggio
Pablo Montalvo
AAML
8
23
0
19 Feb 2018
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A
  Survey
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Naveed Akhtar
Ajmal Mian
AAML
22
1,854
0
02 Jan 2018
A General Framework for Adversarial Examples with Objectives
A General Framework for Adversarial Examples with Objectives
Mahmood Sharif
Sruti Bhagavatula
Lujo Bauer
Michael K. Reiter
AAML
GAN
13
191
0
31 Dec 2017
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