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FAR: A General Framework for Attributional Robustness

FAR: A General Framework for Attributional Robustness

14 October 2020
Adam Ivankay
Ivan Girardi
Chiara Marchiori
P. Frossard
    OOD
ArXivPDFHTML

Papers citing "FAR: A General Framework for Attributional Robustness"

17 / 17 papers shown
Title
Reflective-Net: Learning from Explanations
Reflective-Net: Learning from Explanations
Johannes Schneider
Michalis Vlachos
FAtt
OffRL
LRM
81
17
0
27 Nov 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
493
42,449
0
03 Dec 2019
Attributional Robustness Training using Input-Gradient Spatial Alignment
Attributional Robustness Training using Input-Gradient Spatial Alignment
M. Singh
Nupur Kumari
Puneet Mangla
Abhishek Sinha
V. Balasubramanian
Balaji Krishnamurthy
OOD
69
10
0
29 Nov 2019
Explanations can be manipulated and geometry is to blame
Explanations can be manipulated and geometry is to blame
Ann-Kathrin Dombrowski
Maximilian Alber
Christopher J. Anders
M. Ackermann
K. Müller
Pan Kessel
AAML
FAtt
81
332
0
19 Jun 2019
Robust Attribution Regularization
Robust Attribution Regularization
Jiefeng Chen
Xi Wu
Vaibhav Rastogi
Yingyu Liang
S. Jha
OOD
47
83
0
23 May 2019
On the Connection Between Adversarial Robustness and Saliency Map
  Interpretability
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
Christian Etmann
Sebastian Lunz
Peter Maass
Carola-Bibiane Schönlieb
AAML
FAtt
58
161
0
10 May 2019
Interpretation of Neural Networks is Fragile
Interpretation of Neural Networks is Fragile
Amirata Ghorbani
Abubakar Abid
James Zou
FAtt
AAML
133
866
0
29 Oct 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
  Algorithms
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao
Kashif Rasul
Roland Vollgraf
283
8,883
0
25 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
307
12,069
0
19 Jun 2017
SmoothGrad: removing noise by adding noise
SmoothGrad: removing noise by adding noise
D. Smilkov
Nikhil Thorat
Been Kim
F. Viégas
Martin Wattenberg
FAtt
ODL
201
2,221
0
12 Jun 2017
Learning Important Features Through Propagating Activation Differences
Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar
Peyton Greenside
A. Kundaje
FAtt
198
3,873
0
10 Apr 2017
Right for the Right Reasons: Training Differentiable Models by
  Constraining their Explanations
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
A. Ross
M. C. Hughes
Finale Doshi-Velez
FAtt
120
589
0
10 Mar 2017
Axiomatic Attribution for Deep Networks
Axiomatic Attribution for Deep Networks
Mukund Sundararajan
Ankur Taly
Qiqi Yan
OOD
FAtt
188
5,989
0
04 Mar 2017
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,020
0
10 Dec 2015
Delving Deep into Rectifiers: Surpassing Human-Level Performance on
  ImageNet Classification
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
VLM
323
18,625
0
06 Feb 2015
Deep Inside Convolutional Networks: Visualising Image Classification
  Models and Saliency Maps
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan
Andrea Vedaldi
Andrew Zisserman
FAtt
312
7,295
0
20 Dec 2013
Visualizing and Understanding Convolutional Networks
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler
Rob Fergus
FAtt
SSL
595
15,882
0
12 Nov 2013
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