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Approximating CNNs with Bag-of-local-Features models works surprisingly
  well on ImageNet

Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet

20 March 2019
Wieland Brendel
Matthias Bethge
    SSLFAtt
ArXiv (abs)PDFHTML

Papers citing "Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet"

12 / 312 papers shown
Title
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian
  Augmentation
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation
Raphael Gontijo-Lopes
Dong Yin
Ben Poole
Justin Gilmer
E. D. Cubuk
AAML
181
205
0
06 Jun 2019
Interpreting Adversarially Trained Convolutional Neural Networks
Interpreting Adversarially Trained Convolutional Neural Networks
Tianyuan Zhang
Zhanxing Zhu
AAMLGANFAtt
125
161
0
23 May 2019
What Do Adversarially Robust Models Look At?
What Do Adversarially Robust Models Look At?
Takahiro Itazuri
Yoshihiro Fukuhara
Hirokatsu Kataoka
Shigeo Morishima
32
5
0
19 May 2019
Batch Normalization is a Cause of Adversarial Vulnerability
Batch Normalization is a Cause of Adversarial Vulnerability
A. Galloway
A. Golubeva
T. Tanay
M. Moussa
Graham W. Taylor
ODLAAML
84
80
0
06 May 2019
An Analysis of Pre-Training on Object Detection
An Analysis of Pre-Training on Object Detection
Hengduo Li
Bharat Singh
Mahyar Najibi
Zuxuan Wu
L. Davis
ObjD
60
39
0
11 Apr 2019
Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan
  Hyperpartisan News Detector
Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector
M. Drissi
Pedro Sandoval Segura
Vivaswat Ojha
J. Medero
16
6
0
10 Apr 2019
Deep Learning Under the Microscope: Improving the Interpretability of
  Medical Imaging Neural Networks
Deep Learning Under the Microscope: Improving the Interpretability of Medical Imaging Neural Networks
Magdalini Paschali
Muhammad Ferjad Naeem
Walter Simson
K. Steiger
M. Mollenhauer
Nassir Navab
58
19
0
05 Apr 2019
Recent Advances in Natural Language Inference: A Survey of Benchmarks,
  Resources, and Approaches
Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches
Shane Storks
Qiaozi Gao
J. Chai
100
132
0
02 Apr 2019
SRM : A Style-based Recalibration Module for Convolutional Neural
  Networks
SRM : A Style-based Recalibration Module for Convolutional Neural Networks
HyunJae Lee
Hyo-Eun Kim
Hyeonseob Nam
72
228
0
26 Mar 2019
What the Constant Velocity Model Can Teach Us About Pedestrian Motion
  Prediction
What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction
Christoph Schöller
Vincent Aravantinos
F. Lay
Alois C. Knoll
74
224
0
19 Mar 2019
Augmenting Model Robustness with Transformation-Invariant Attacks
Augmenting Model Robustness with Transformation-Invariant Attacks
Houpu Yao
Zhe Wang
Guangyu Nie
Yassine Mazboudi
Yezhou Yang
Yi Ren
AAMLOOD
31
3
0
31 Jan 2019
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
221
2,682
0
29 Nov 2018
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