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Network Dissection: Quantifying Interpretability of Deep Visual
  Representations

Network Dissection: Quantifying Interpretability of Deep Visual Representations

19 April 2017
David Bau
Bolei Zhou
A. Khosla
A. Oliva
Antonio Torralba
    MILMFAtt
ArXiv (abs)PDFHTML

Papers citing "Network Dissection: Quantifying Interpretability of Deep Visual Representations"

50 / 787 papers shown
Title
Cost-effective Interactive Attention Learning with Neural Attention
  Processes
Cost-effective Interactive Attention Learning with Neural Attention Processes
Jay Heo
Junhyeong Park
Hyewon Jeong
Kwang Joon Kim
Juho Lee
Eunho Yang
Sung Ju Hwang
48
8
0
09 Jun 2020
Low Distortion Block-Resampling with Spatially Stochastic Networks
Low Distortion Block-Resampling with Spatially Stochastic Networks
S. J. Hong
Martín Arjovsky
Darryl Barnhart
Ian Thompson
48
8
0
09 Jun 2020
Black-box Explanation of Object Detectors via Saliency Maps
Black-box Explanation of Object Detectors via Saliency Maps
Vitali Petsiuk
R. Jain
Varun Manjunatha
Vlad I. Morariu
Ashutosh Mehra
Vicente Ordonez
Kate Saenko
FAtt
81
125
0
05 Jun 2020
Learning to Branch for Multi-Task Learning
Learning to Branch for Multi-Task Learning
Pengsheng Guo
Chen-Yu Lee
Daniel Ulbricht
104
179
0
02 Jun 2020
Explainable Artificial Intelligence: a Systematic Review
Explainable Artificial Intelligence: a Systematic Review
Giulia Vilone
Luca Longo
XAI
110
271
0
29 May 2020
Network Bending: Expressive Manipulation of Deep Generative Models
Network Bending: Expressive Manipulation of Deep Generative Models
Terence Broad
F. Leymarie
M. Grierson
AI4CE
39
2
0
25 May 2020
Interpretable and Accurate Fine-grained Recognition via Region Grouping
Interpretable and Accurate Fine-grained Recognition via Region Grouping
Zixuan Huang
Yin Li
85
141
0
21 May 2020
Finding Experts in Transformer Models
Finding Experts in Transformer Models
Xavier Suau
Luca Zappella
N. Apostoloff
60
31
0
15 May 2020
Compositional Few-Shot Recognition with Primitive Discovery and
  Enhancing
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing
Yixiong Zou
Shanghang Zhang
Ke Chen
Yonghong Tian
Yaowei Wang
J. M. F. Moura
49
27
0
12 May 2020
Explaining AI-based Decision Support Systems using Concept Localization
  Maps
Explaining AI-based Decision Support Systems using Concept Localization Maps
Adriano Lucieri
Muhammad Naseer Bajwa
Andreas Dengel
Sheraz Ahmed
72
27
0
04 May 2020
A Disentangling Invertible Interpretation Network for Explaining Latent
  Representations
A Disentangling Invertible Interpretation Network for Explaining Latent Representations
Patrick Esser
Robin Rombach
Bjorn Ommer
62
88
0
27 Apr 2020
Interpretation of Deep Temporal Representations by Selective
  Visualization of Internally Activated Nodes
Interpretation of Deep Temporal Representations by Selective Visualization of Internally Activated Nodes
Sohee Cho
Ginkyeng Lee
Wonjoon Chang
Jaesik Choi
73
16
0
27 Apr 2020
Games for Fairness and Interpretability
Games for Fairness and Interpretability
Eric Chu
Nabeel Gillani
S. Makini
FaML
32
4
0
20 Apr 2020
Motion-supervised Co-Part Segmentation
Motion-supervised Co-Part Segmentation
Aliaksandr Siarohin
Subhankar Roy
Stéphane Lathuilière
Sergey Tulyakov
Elisa Ricci
N. Sebe
SSL
48
35
0
07 Apr 2020
Under the Hood of Neural Networks: Characterizing Learned
  Representations by Functional Neuron Populations and Network Ablations
Under the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network Ablations
Richard Meyes
Constantin Waubert de Puiseau
Andres Felipe Posada-Moreno
Tobias Meisen
AI4CE
78
22
0
02 Apr 2020
Architecture Disentanglement for Deep Neural Networks
Architecture Disentanglement for Deep Neural Networks
Jie Hu
Liujuan Cao
QiXiang Ye
Tong Tong
Shengchuan Zhang
Ke Li
Feiyue Huang
Rongrong Ji
Ling Shao
AAML
88
18
0
30 Mar 2020
A Survey of Deep Learning for Scientific Discovery
A Survey of Deep Learning for Scientific Discovery
M. Raghu
Erica Schmidt
OODAI4CE
182
123
0
26 Mar 2020
Deep Grouping Model for Unified Perceptual Parsing
Deep Grouping Model for Unified Perceptual Parsing
Zhiheng Li
Wenxuan Bao
Jiayang Zheng
Chenliang Xu
150
13
0
25 Mar 2020
Foundations of Explainable Knowledge-Enabled Systems
Foundations of Explainable Knowledge-Enabled Systems
Shruthi Chari
Daniel Gruen
Oshani Seneviratne
D. McGuinness
82
29
0
17 Mar 2020
Self-Supervised Discovering of Interpretable Features for Reinforcement
  Learning
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning
Wenjie Shi
Gao Huang
Shiji Song
Zhuoyuan Wang
Tingyu Lin
Cheng Wu
SSL
88
18
0
16 Mar 2020
Explaining Knowledge Distillation by Quantifying the Knowledge
Explaining Knowledge Distillation by Quantifying the Knowledge
Xu Cheng
Zhefan Rao
Yilan Chen
Quanshi Zhang
83
122
0
07 Mar 2020
TIME: A Transparent, Interpretable, Model-Adaptive and Explainable
  Neural Network for Dynamic Physical Processes
TIME: A Transparent, Interpretable, Model-Adaptive and Explainable Neural Network for Dynamic Physical Processes
Gurpreet Singh
Soumyajit Gupta
Matt Lease
Clint Dawson
AI4TSAI4CE
24
2
0
05 Mar 2020
What's the relationship between CNNs and communication systems?
What's the relationship between CNNs and communication systems?
Hao Ge
X. Tu
Yanxiang Gong
M. Xie
Zheng Ma
20
0
0
03 Mar 2020
Selectivity considered harmful: evaluating the causal impact of class
  selectivity in DNNs
Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs
Matthew L. Leavitt
Ari S. Morcos
110
33
0
03 Mar 2020
On Leveraging Pretrained GANs for Generation with Limited Data
On Leveraging Pretrained GANs for Generation with Limited Data
Miaoyun Zhao
Yulai Cong
Lawrence Carin
107
21
0
26 Feb 2020
Neuron Shapley: Discovering the Responsible Neurons
Neuron Shapley: Discovering the Responsible Neurons
Amirata Ghorbani
James Zou
FAttTDI
63
115
0
23 Feb 2020
Sampling for Deep Learning Model Diagnosis (Technical Report)
Sampling for Deep Learning Model Diagnosis (Technical Report)
Parmita Mehta
S. Portillo
Magdalena Balazinska
Andrew J. Connolly
LM&MAMLAU
31
2
0
22 Feb 2020
Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by
  Example
Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example
Serena Booth
Yilun Zhou
Ankit J. Shah
J. Shah
BDL
42
2
0
19 Feb 2020
Classifying the classifier: dissecting the weight space of neural
  networks
Classifying the classifier: dissecting the weight space of neural networks
Gabriel Eilertsen
Daniel Jonsson
Timo Ropinski
Jonas Unger
Anders Ynnerman
71
54
0
13 Feb 2020
CHAIN: Concept-harmonized Hierarchical Inference Interpretation of Deep
  Convolutional Neural Networks
CHAIN: Concept-harmonized Hierarchical Inference Interpretation of Deep Convolutional Neural Networks
Dan Wang
Xinrui Cui
F. I. Z. Jane Wang
AI4CE
31
14
0
05 Feb 2020
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for
  Sequential Decision-Making Problems with Inscrutable Representations
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations
S. Sreedharan
Utkarsh Soni
Mudit Verma
Siddharth Srivastava
S. Kambhampati
130
31
0
04 Feb 2020
Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and
  Occlusion
Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusion
Wenbo Dong
Pravakar Roy
Cheng Peng
Volkan Isler
73
65
0
30 Jan 2020
Factors Influencing Perceived Fairness in Algorithmic Decision-Making:
  Algorithm Outcomes, Development Procedures, and Individual Differences
Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences
Ruotong Wang
F. M. Harper
Haiyi Zhu
FaML
66
187
0
27 Jan 2020
Adapting Grad-CAM for Embedding Networks
Adapting Grad-CAM for Embedding Networks
Lei Chen
Jianhui Chen
Hossein Hajimirsadeghi
Greg Mori
78
57
0
17 Jan 2020
Keeping Community in the Loop: Understanding Wikipedia Stakeholder
  Values for Machine Learning-Based Systems
Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems
C. E. Smith
Bowen Yu
Anjali Srivastava
Aaron L Halfaker
Loren G. Terveen
Haiyi Zhu
KELM
126
70
0
14 Jan 2020
Boosting Occluded Image Classification via Subspace Decomposition Based
  Estimation of Deep Features
Boosting Occluded Image Classification via Subspace Decomposition Based Estimation of Deep Features
Feng Cen
Guanghui Wang
87
32
0
13 Jan 2020
On Interpretability of Artificial Neural Networks: A Survey
On Interpretability of Artificial Neural Networks: A Survey
Fenglei Fan
Jinjun Xiong
Mengzhou Li
Ge Wang
AAMLAI4CE
94
318
0
08 Jan 2020
Image Enhanced Rotation Prediction for Self-Supervised Learning
Image Enhanced Rotation Prediction for Self-Supervised Learning
Shin'ya Yamaguchi
Sekitoshi Kanai
Tetsuya Shioda
Shoichiro Takeda
49
12
0
25 Dec 2019
White Noise Analysis of Neural Networks
White Noise Analysis of Neural Networks
Ali Borji
Sikun Lin
FAtt
53
12
0
23 Dec 2019
Analysis of Video Feature Learning in Two-Stream CNNs on the Example of
  Zebrafish Swim Bout Classification
Analysis of Video Feature Learning in Two-Stream CNNs on the Example of Zebrafish Swim Bout Classification
Bennet Breier
A. Onken
36
4
0
20 Dec 2019
Semantic Segmentation from Remote Sensor Data and the Exploitation of
  Latent Learning for Classification of Auxiliary Tasks
Semantic Segmentation from Remote Sensor Data and the Exploitation of Latent Learning for Classification of Auxiliary Tasks
B. Chatterjee
Charalambos (Charis) Poullis
SSeg
33
17
0
19 Dec 2019
Embedding Comparator: Visualizing Differences in Global Structure and
  Local Neighborhoods via Small Multiples
Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples
Angie Boggust
Brandon Carter
Arvind Satyanarayan
102
65
0
10 Dec 2019
Frivolous Units: Wider Networks Are Not Really That Wide
Frivolous Units: Wider Networks Are Not Really That Wide
Stephen Casper
Xavier Boix
Vanessa D’Amario
Ling Guo
Martin Schrimpf
Kasper Vinken
Gabriel Kreiman
71
19
0
10 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
98
10
0
29 Nov 2019
Orthogonal Convolutional Neural Networks
Orthogonal Convolutional Neural Networks
Jiayun Wang
Yubei Chen
Rudrasis Chakraborty
Stella X. Yu
89
190
0
27 Nov 2019
Analysis of Explainers of Black Box Deep Neural Networks for Computer
  Vision: A Survey
Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey
Vanessa Buhrmester
David Münch
Michael Arens
MLAUFaMLXAIAAML
123
369
0
27 Nov 2019
Furnishing Your Room by What You See: An End-to-End Furniture Set
  Retrieval Framework with Rich Annotated Benchmark Dataset
Furnishing Your Room by What You See: An End-to-End Furniture Set Retrieval Framework with Rich Annotated Benchmark Dataset
Bingyuan Liu
Jiantao Zhang
Xiaoting Zhang
Wei Zhang
Chuanhui Yu
Yuan Zhou
3DPC3DV
56
7
0
21 Nov 2019
Semantic Hierarchy Emerges in Deep Generative Representations for Scene
  Synthesis
Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis
Ceyuan Yang
Yujun Shen
Bolei Zhou
GAN
174
202
0
21 Nov 2019
Towards a Unified Evaluation of Explanation Methods without Ground Truth
Towards a Unified Evaluation of Explanation Methods without Ground Truth
Hao Zhang
Jiayi Chen
Haotian Xue
Quanshi Zhang
XAI
71
8
0
20 Nov 2019
DRNet: Dissect and Reconstruct the Convolutional Neural Network via
  Interpretable Manners
DRNet: Dissect and Reconstruct the Convolutional Neural Network via Interpretable Manners
Xiaolong Hu
Zhulin An
Chuanguang Yang
Hui Zhu
Kaiqiang Xu
Yongjun Xu
45
3
0
20 Nov 2019
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