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1704.05796
Cited By
Network Dissection: Quantifying Interpretability of Deep Visual Representations
19 April 2017
David Bau
Bolei Zhou
A. Khosla
A. Oliva
Antonio Torralba
MILM
FAtt
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Papers citing
"Network Dissection: Quantifying Interpretability of Deep Visual Representations"
50 / 842 papers shown
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Learning Interpretable Concept Groups in CNNs
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Biagio La Rosa
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Cross-Model Consensus of Explanations and Beyond for Image Classification Models: An Empirical Study
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133
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Wanying Tao
Jianfei Xing
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141
3
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Interpreting Face Inference Models using Hierarchical Network Dissection
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408
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Towards Interpretable Deep Networks for Monocular Depth Estimation
IEEE International Conference on Computer Vision (ICCV), 2021
Zunzhi You
Yi-Hsuan Tsai
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173
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COVID-view: Diagnosis of COVID-19 using Chest CT
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Shreeraj Jadhav
Gaofeng Deng
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Spatiotemporal Contrastive Learning of Facial Expressions in Videos
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Manli Shu
Eitan Borgnia
Furong Huang
Micah Goldblum
Tom Goldstein
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118
12
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03 Aug 2021
Shared Interest: Measuring Human-AI Alignment to Identify Recurring Patterns in Model Behavior
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Angie Boggust
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Arvindmani Satyanarayan
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193
60
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Ghassan Hamarneh
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203
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Using Causal Analysis for Conceptual Deep Learning Explanation
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021
Sumedha Singla
Stephen Wallace
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Kayhan Batmanghelich
CML
106
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Interpretable Compositional Convolutional Neural Networks
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Zhihua Wei
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164
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201
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Wynne Hsu
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114
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24 Jun 2021
Evaluation of Saliency-based Explainability Method
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V. Kamakshi
Namrata Lodhi
N. C. Krishnan
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158
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24 Jun 2021
Visual Probing: Cognitive Framework for Explaining Self-Supervised Image Representations
IEEE Access (IEEE Access), 2021
Witold Oleszkiewicz
Dominika Basaj
Igor Sieradzki
Michal Górszczak
Barbara Rychalska
K. Lewandowska
Tomasz Trzciñski
Bartosz Zieliñski
SSL
196
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21 Jun 2021
A Game-Theoretic Taxonomy of Visual Concepts in DNNs
Feng He
Chuntung Chu
Yi Zheng
Jie Ren
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107
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21 Jun 2021
Cogradient Descent for Dependable Learning
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Baochang Zhang
Lian Zhuo
QiXiang Ye
David Doermann
120
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Guided Integrated Gradients: An Adaptive Path Method for Removing Noise
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A. Kapishnikov
Subhashini Venugopalan
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Michael Terry
Tolga Bolukbasi
269
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Best of both worlds: local and global explanations with human-understandable concepts
Jessica Schrouff
Sebastien Baur
Shaobo Hou
Diana Mincu
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Ralph Blanes
James Wexler
Alan Karthikesalingam
Been Kim
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234
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16 Jun 2021
On the Evolution of Neuron Communities in a Deep Learning Architecture
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Debajyoti Mondal
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182
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08 Jun 2021
3DB: A Framework for Debugging Computer Vision Models
Neural Information Processing Systems (NeurIPS), 2021
Guillaume Leclerc
Hadi Salman
Andrew Ilyas
Sai H. Vemprala
Logan Engstrom
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Shibani Santurkar
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Ashish Kapoor
Aleksander Madry
244
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Improving Compositionality of Neural Networks by Decoding Representations to Inputs
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Noah D. Goodman
Stefano Ermon
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124
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Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machine
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Rohan Kumar Yadav
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Lei Jiao
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212
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The Definitions of Interpretability and Learning of Interpretable Models
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Transparent Model of Unabridged Data (TMUD)
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107
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A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts
Data mining and knowledge discovery (DMKD), 2021
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The Low-Dimensional Linear Geometry of Contextualized Word Representations
Conference on Computational Natural Language Learning (CoNLL), 2021
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Jacob Andreas
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244
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Cause and Effect: Hierarchical Concept-based Explanation of Neural Networks
IEEE International Conference on Systems, Man and Cybernetics (SMC), 2021
Mohammad Nokhbeh Zaeem
Majid Komeili
CML
194
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Verification of Size Invariance in DNN Activations using Concept Embeddings
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XAI Handbook: Towards a Unified Framework for Explainable AI
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Adriano Lucieri
Mohsin Munir
Jörn Hees
Sheraz Ahmed
Andreas Dengel
137
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Boosting Light-Weight Depth Estimation Via Knowledge Distillation
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Chenyou Fan
Hualie Jiang
Xiyue Guo
Yuan Gao
Xiangyong Lu
Tin Lun Lam
217
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Leveraging Sparse Linear Layers for Debuggable Deep Networks
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Shibani Santurkar
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213
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Rationalization through Concepts
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Boi Faltings
FAtt
214
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This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks
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Claudio Fanconi
Rahul Rade
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255
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Do Feature Attribution Methods Correctly Attribute Features?
AAAI Conference on Artificial Intelligence (AAAI), 2021
Yilun Zhou
Serena Booth
Marco Tulio Ribeiro
J. Shah
FAtt
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412
155
0
27 Apr 2021
Exploiting Explanations for Model Inversion Attacks
IEEE International Conference on Computer Vision (ICCV), 2021
Xu Zhao
Wencan Zhang
Xiao Xiao
Brian Y. Lim
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328
105
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26 Apr 2021
EigenGAN: Layer-Wise Eigen-Learning for GANs
IEEE International Conference on Computer Vision (ICCV), 2021
Zhenliang He
Meina Kan
Shiguang Shan
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213
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0
26 Apr 2021
Neural Mean Discrepancy for Efficient Out-of-Distribution Detection
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Xin Dong
Junfeng Guo
Ang Li
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H. T. Kung
OODD
337
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0
23 Apr 2021
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