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Salient ImageNet: How to discover spurious features in Deep Learning?

Salient ImageNet: How to discover spurious features in Deep Learning?

8 October 2021
Sahil Singla
Soheil Feizi
    AAML
    VLM
ArXivPDFHTML

Papers citing "Salient ImageNet: How to discover spurious features in Deep Learning?"

43 / 43 papers shown
Title
Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization
Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization
Simone Bombari
Marco Mondelli
177
0
0
03 Feb 2025
Sebra: Debiasing Through Self-Guided Bias Ranking
Sebra: Debiasing Through Self-Guided Bias Ranking
Adarsh Kappiyath
Abhra Chaudhuri
Ajay Jaiswal
Ziquan Liu
Yunpeng Li
Xiatian Zhu
L. Yin
CML
347
1
1
30 Jan 2025
3DB: A Framework for Debugging Computer Vision Models
3DB: A Framework for Debugging Computer Vision Models
Guillaume Leclerc
Hadi Salman
Andrew Ilyas
Sai H. Vemprala
Logan Engstrom
...
Pengchuan Zhang
Shibani Santurkar
Greg Yang
Ashish Kapoor
Aleksander Madry
57
40
0
07 Jun 2021
Finding and Fixing Spurious Patterns with Explanations
Finding and Fixing Spurious Patterns with Explanations
Gregory Plumb
Marco Tulio Ribeiro
Ameet Talwalkar
60
41
0
03 Jun 2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks
Leveraging Sparse Linear Layers for Debuggable Deep Networks
Eric Wong
Shibani Santurkar
Aleksander Madry
FAtt
44
90
0
11 May 2021
Removing Spurious Features can Hurt Accuracy and Affect Groups
  Disproportionately
Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately
Fereshte Khani
Percy Liang
FaML
39
65
0
07 Dec 2020
Understanding Failures of Deep Networks via Robust Feature Extraction
Understanding Failures of Deep Networks via Robust Feature Extraction
Sahil Singla
Besmira Nushi
S. Shah
Ece Kamar
Eric Horvitz
FAtt
45
83
0
03 Dec 2020
Benchmarking Deep Learning Interpretability in Time Series Predictions
Benchmarking Deep Learning Interpretability in Time Series Predictions
Aya Abdelsalam Ismail
Mohamed K. Gunady
H. C. Bravo
Soheil Feizi
XAI
AI4TS
FAtt
31
169
0
26 Oct 2020
Understanding the Role of Individual Units in a Deep Neural Network
Understanding the Role of Individual Units in a Deep Neural Network
David Bau
Jun-Yan Zhu
Hendrik Strobelt
Àgata Lapedriza
Bolei Zhou
Antonio Torralba
GAN
52
446
0
10 Sep 2020
Generative causal explanations of black-box classifiers
Generative causal explanations of black-box classifiers
Matthew R. O’Shaughnessy
Gregory H. Canal
Marissa Connor
Mark A. Davenport
Christopher Rozell
CML
45
73
0
24 Jun 2020
Noise or Signal: The Role of Image Backgrounds in Object Recognition
Noise or Signal: The Role of Image Backgrounds in Object Recognition
Kai Y. Xiao
Logan Engstrom
Andrew Ilyas
Aleksander Madry
119
384
0
17 Jun 2020
From ImageNet to Image Classification: Contextualizing Progress on
  Benchmarks
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
Dimitris Tsipras
Shibani Santurkar
Logan Engstrom
Andrew Ilyas
Aleksander Madry
53
132
0
22 May 2020
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
Marco Tulio Ribeiro
Tongshuang Wu
Carlos Guestrin
Sameer Singh
ELM
145
1,089
0
08 May 2020
Debiasing Skin Lesion Datasets and Models? Not So Fast
Debiasing Skin Lesion Datasets and Models? Not So Fast
Alceu Bissoto
Eduardo Valle
Sandra Avila
78
55
0
23 Apr 2020
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural
  Networks
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks
Aya Abdelsalam Ismail
Mohamed K. Gunady
L. Pessoa
H. C. Bravo
Soheil Feizi
AI4TS
59
50
0
27 Oct 2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan
Quoc V. Le
3DV
MedIm
123
17,950
0
28 May 2019
Understanding Impacts of High-Order Loss Approximations and Features in
  Deep Learning Interpretation
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation
Sahil Singla
Eric Wallace
Shi Feng
Soheil Feizi
FAtt
50
59
0
01 Feb 2019
On the (In)fidelity and Sensitivity for Explanations
On the (In)fidelity and Sensitivity for Explanations
Chih-Kuan Yeh
Cheng-Yu Hsieh
A. Suggala
David I. Inouye
Pradeep Ravikumar
FAtt
58
449
0
27 Jan 2019
Sanity Checks for Saliency Maps
Sanity Checks for Saliency Maps
Julius Adebayo
Justin Gilmer
M. Muelly
Ian Goodfellow
Moritz Hardt
Been Kim
FAtt
AAML
XAI
118
1,947
0
08 Oct 2018
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing
  System Failure
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure
Besmira Nushi
Ece Kamar
Eric Horvitz
32
140
0
19 Sep 2018
Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of
  Machine Learning Models
Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models
Jiawei Zhang
Yang Wang
Piero Molino
Lezhi Li
D. Ebert
FAtt
46
203
0
01 Aug 2018
Explaining Image Classifiers by Counterfactual Generation
Explaining Image Classifiers by Counterfactual Generation
C. Chang
Elliot Creager
Anna Goldenberg
David Duvenaud
VLM
51
265
0
20 Jul 2018
Automated Data Slicing for Model Validation:A Big data - AI Integration
  Approach
Automated Data Slicing for Model Validation:A Big data - AI Integration Approach
Yeounoh Chung
Tim Kraska
N. Polyzotis
Ki Hyun Tae
Steven Euijong Whang
61
131
0
16 Jul 2018
Recognition in Terra Incognita
Recognition in Terra Incognita
Sara Beery
Grant Van Horn
Pietro Perona
78
835
0
13 Jul 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
88
1,772
0
30 May 2018
Interpreting Deep Visual Representations via Network Dissection
Interpreting Deep Visual Representations via Network Dissection
Bolei Zhou
David Bau
A. Oliva
Antonio Torralba
FAtt
MILM
53
324
0
15 Nov 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
238
11,962
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
192
2,215
0
12 Jun 2017
Understanding Black-box Predictions via Influence Functions
Understanding Black-box Predictions via Influence Functions
Pang Wei Koh
Percy Liang
TDI
142
2,854
0
14 Mar 2017
Axiomatic Attribution for Deep Networks
Axiomatic Attribution for Deep Networks
Mukund Sundararajan
Ankur Taly
Qiqi Yan
OOD
FAtt
142
5,920
0
04 Mar 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based
  Localization
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Ramprasaath R. Selvaraju
Michael Cogswell
Abhishek Das
Ramakrishna Vedantam
Devi Parikh
Dhruv Batra
FAtt
225
19,796
0
07 Oct 2016
Wide Residual Networks
Wide Residual Networks
Sergey Zagoruyko
N. Komodakis
277
7,951
0
23 May 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAtt
FaML
681
16,828
0
16 Feb 2016
Multifaceted Feature Visualization: Uncovering the Different Types of
  Features Learned By Each Neuron in Deep Neural Networks
Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks
Anh Totti Nguyen
J. Yosinski
Jeff Clune
49
327
0
11 Feb 2016
Learning Deep Features for Discriminative Localization
Learning Deep Features for Discriminative Localization
Bolei Zhou
A. Khosla
Àgata Lapedriza
A. Oliva
Antonio Torralba
SSL
SSeg
FAtt
186
9,280
0
14 Dec 2015
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
1.5K
192,638
0
10 Dec 2015
Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images
Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images
Aravindh Mahendran
Andrea Vedaldi
FAtt
61
532
0
07 Dec 2015
Understanding Neural Networks Through Deep Visualization
Understanding Neural Networks Through Deep Visualization
J. Yosinski
Jeff Clune
Anh Totti Nguyen
Thomas J. Fuchs
Hod Lipson
FAtt
AI4CE
104
1,866
0
22 Jun 2015
Inverting Visual Representations with Convolutional Networks
Inverting Visual Representations with Convolutional Networks
Alexey Dosovitskiy
Thomas Brox
SSL
FAtt
61
662
0
09 Jun 2015
Deep Neural Networks are Easily Fooled: High Confidence Predictions for
  Unrecognizable Images
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Anh Totti Nguyen
J. Yosinski
Jeff Clune
AAML
136
3,261
0
05 Dec 2014
Understanding Deep Image Representations by Inverting Them
Understanding Deep Image Representations by Inverting Them
Aravindh Mahendran
Andrea Vedaldi
FAtt
94
1,959
0
26 Nov 2014
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
207
7,252
0
20 Dec 2013
Visualizing and Understanding Convolutional Networks
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler
Rob Fergus
FAtt
SSL
376
15,825
0
12 Nov 2013
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