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An Empirical Evaluation on Robustness and Uncertainty of Regularization
  Methods

An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods

9 March 2020
Sanghyuk Chun
Seong Joon Oh
Sangdoo Yun
Dongyoon Han
Junsuk Choe
Y. Yoo
    AAML
    OOD
ArXivPDFHTML

Papers citing "An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods"

41 / 41 papers shown
Title
CutMix: Regularization Strategy to Train Strong Classifiers with
  Localizable Features
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Sangdoo Yun
Dongyoon Han
Seong Joon Oh
Sanghyuk Chun
Junsuk Choe
Y. Yoo
OOD
492
4,728
0
13 May 2019
Benchmarking Neural Network Robustness to Common Corruptions and
  Perturbations
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Dan Hendrycks
Thomas G. Dietterich
OOD
VLM
17
3,386
0
28 Mar 2019
Feature Denoising for Improving Adversarial Robustness
Feature Denoising for Improving Adversarial Robustness
Cihang Xie
Yuxin Wu
Laurens van der Maaten
Alan Yuille
Kaiming He
49
905
0
09 Dec 2018
ImageNet-trained CNNs are biased towards texture; increasing shape bias
  improves accuracy and robustness
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos
Patricia Rubisch
Claudio Michaelis
Matthias Bethge
Felix Wichmann
Wieland Brendel
44
2,638
0
29 Nov 2018
DropBlock: A regularization method for convolutional networks
DropBlock: A regularization method for convolutional networks
Golnaz Ghiasi
Nayeon Lee
Quoc V. Le
68
908
0
30 Oct 2018
Gather-Excite: Exploiting Feature Context in Convolutional Neural
  Networks
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks
Jie Hu
Li Shen
Samuel Albanie
Gang Sun
Andrea Vedaldi
28
573
0
29 Oct 2018
NSML: Meet the MLaaS platform with a real-world case study
NSML: Meet the MLaaS platform with a real-world case study
Hanjoo Kim
Minkyu Kim
Dongjoo Seo
Jinwoong Kim
Heungseok Park
...
KyungHyun Kim
Youngil Yang
Youngkwan Kim
Nako Sung
Jung-Woo Ha
25
131
0
08 Oct 2018
Generalisation in humans and deep neural networks
Generalisation in humans and deep neural networks
Robert Geirhos
Carlos R. Medina Temme
Jonas Rauber
Heiko H. Schutt
Matthias Bethge
Felix Wichmann
OOD
59
604
0
27 Aug 2018
Robustness May Be at Odds with Accuracy
Robustness May Be at Odds with Accuracy
Dimitris Tsipras
Shibani Santurkar
Logan Engstrom
Alexander Turner
Aleksander Madry
AAML
44
1,765
0
30 May 2018
AutoAugment: Learning Augmentation Policies from Data
AutoAugment: Learning Augmentation Policies from Data
E. D. Cubuk
Barret Zoph
Dandelion Mané
Vijay Vasudevan
Quoc V. Le
73
1,760
0
24 May 2018
Adversarial Logit Pairing
Adversarial Logit Pairing
Harini Kannan
Alexey Kurakin
Ian Goodfellow
AAML
48
626
0
16 Mar 2018
ShakeDrop Regularization for Deep Residual Learning
ShakeDrop Regularization for Deep Residual Learning
Yoshihiro Yamada
Masakazu Iwamura
Takuya Akiba
K. Kise
39
162
0
07 Feb 2018
NSML: A Machine Learning Platform That Enables You to Focus on Your
  Models
NSML: A Machine Learning Platform That Enables You to Focus on Your Models
Nako Sung
Minkyu Kim
Hyunwoo Jo
Youngil Yang
Jingwoong Kim
...
Youngkwan Kim
Gayoung Lee
Donghyun Kwak
Jung-Woo Ha
Sunghun Kim
43
86
0
16 Dec 2017
Training Confidence-calibrated Classifiers for Detecting
  Out-of-Distribution Samples
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
Kimin Lee
Honglak Lee
Kibok Lee
Jinwoo Shin
OODD
77
878
0
26 Nov 2017
Attacking the Madry Defense Model with $L_1$-based Adversarial Examples
Attacking the Madry Defense Model with L1L_1L1​-based Adversarial Examples
Yash Sharma
Pin-Yu Chen
34
118
0
30 Oct 2017
mixup: Beyond Empirical Risk Minimization
mixup: Beyond Empirical Risk Minimization
Hongyi Zhang
Moustapha Cissé
Yann N. Dauphin
David Lopez-Paz
NoLa
191
9,663
0
25 Oct 2017
Squeeze-and-Excitation Networks
Squeeze-and-Excitation Networks
Jie Hu
Li Shen
Samuel Albanie
Gang Sun
Enhua Wu
205
26,140
0
05 Sep 2017
Random Erasing Data Augmentation
Random Erasing Data Augmentation
Zhun Zhong
Liang Zheng
Guoliang Kang
Shaozi Li
Yi Yang
53
3,607
0
16 Aug 2017
Improved Regularization of Convolutional Neural Networks with Cutout
Improved Regularization of Convolutional Neural Networks with Cutout
Terrance Devries
Graham W. Taylor
51
3,731
0
15 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILM
OOD
148
11,944
0
19 Jun 2017
On Calibration of Modern Neural Networks
On Calibration of Modern Neural Networks
Chuan Guo
Geoff Pleiss
Yu Sun
Kilian Q. Weinberger
UQCV
84
5,746
0
14 Jun 2017
Enhancing The Reliability of Out-of-distribution Image Detection in
  Neural Networks
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Shiyu Liang
Yixuan Li
R. Srikant
UQCV
OODD
72
2,042
0
08 Jun 2017
Shake-Shake regularization
Shake-Shake regularization
Xavier Gastaldi
3DPC
BDL
OOD
41
380
0
21 May 2017
Improved Training of Wasserstein GANs
Improved Training of Wasserstein GANs
Ishaan Gulrajani
Faruk Ahmed
Martín Arjovsky
Vincent Dumoulin
Aaron Courville
GAN
21
9,497
0
31 Mar 2017
What Uncertainties Do We Need in Bayesian Deep Learning for Computer
  Vision?
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Alex Kendall
Y. Gal
BDL
OOD
UD
UQCV
PER
161
4,656
0
15 Mar 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
345
5,742
0
05 Dec 2016
Adversarial Machine Learning at Scale
Adversarial Machine Learning at Scale
Alexey Kurakin
Ian Goodfellow
Samy Bengio
AAML
397
3,123
0
04 Nov 2016
Deep Pyramidal Residual Networks
Deep Pyramidal Residual Networks
Dongyoon Han
Jiwhan Kim
Junmo Kim
47
689
0
10 Oct 2016
A Baseline for Detecting Misclassified and Out-of-Distribution Examples
  in Neural Networks
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Dan Hendrycks
Kevin Gimpel
UQCV
76
3,411
0
07 Oct 2016
Densely Connected Convolutional Networks
Densely Connected Convolutional Networks
Gao Huang
Zhuang Liu
Laurens van der Maaten
Kilian Q. Weinberger
PINN
3DV
403
36,530
0
25 Aug 2016
Deep Networks with Stochastic Depth
Deep Networks with Stochastic Depth
Gao Huang
Yu Sun
Zhuang Liu
Daniel Sedra
Kilian Q. Weinberger
79
2,342
0
30 Mar 2016
Inception-v4, Inception-ResNet and the Impact of Residual Connections on
  Learning
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Christian Szegedy
Sergey Ioffe
Vincent Vanhoucke
Alexander A. Alemi
196
14,175
0
23 Feb 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
640
192,387
0
10 Dec 2015
Rethinking the Inception Architecture for Computer Vision
Rethinking the Inception Architecture for Computer Vision
Christian Szegedy
Vincent Vanhoucke
Sergey Ioffe
Jonathon Shlens
Z. Wojna
3DV
BDL
178
27,191
0
02 Dec 2015
Unsupervised Representation Learning with Deep Convolutional Generative
  Adversarial Networks
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Alec Radford
Luke Metz
Soumith Chintala
GAN
OOD
193
13,961
0
19 Nov 2015
LSUN: Construction of a Large-scale Image Dataset using Deep Learning
  with Humans in the Loop
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
Feng Yu
Ari Seff
Yinda Zhang
Shuran Song
Thomas Funkhouser
Jianxiong Xiao
27
2,319
0
10 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
313
9,221
0
06 Jun 2015
Batch Normalization: Accelerating Deep Network Training by Reducing
  Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe
Christian Szegedy
OOD
138
43,110
0
11 Feb 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
97
149,279
0
22 Dec 2014
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAML
GAN
93
18,883
0
20 Dec 2014
Intriguing properties of neural networks
Intriguing properties of neural networks
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
41
14,807
1
21 Dec 2013
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