Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks

10 January 2018

Andrea Vedaldi

Papers citing "Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks"

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Title
ChannelExplorer: Exploring Class Separability Through Activation Channel Visualization Md Rahat-uz- Zaman Bei Wang Paul Rosen 28 0 0 06 May 2025
Graphical Perception of Saliency-based Model Explanations Yayan Zhao Mingwei Li Matthew Berger XAI FAtt 49 2 0 11 Jun 2024
Listenable Maps for Zero-Shot Audio Classifiers Francesco Paissan Luca Della Libera Mirco Ravanelli Cem Subakan 40 4 0 27 May 2024
Linear Explanations for Individual Neurons Tuomas P. Oikarinen Tsui-Wei Weng FAtt MILM 31 6 0 10 May 2024
A Multimodal Automated Interpretability Agent Tamar Rott Shaham Sarah Schwettmann Franklin Wang Achyuta Rajaram Evan Hernandez Jacob Andreas Antonio Torralba 39 18 0 22 Apr 2024
Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE) Usha Bhalla Alexander X. Oesterling Suraj Srinivas Flavio du Pin Calmon Himabindu Lakkaraju 49 36 0 16 Feb 2024
PICNN: A Pathway towards Interpretable Convolutional Neural Networks Wengang Guo Jiayi Yang Huilin Yin Qijun Chen Wei Ye 39 3 0 19 Dec 2023
Codebook Features: Sparse and Discrete Interpretability for Neural Networks Alex Tamkin Mohammad Taufeeque Noah D. Goodman 35 27 0 26 Oct 2023
Explaining Deep Face Algorithms through Visualization: A Survey Thrupthi Ann S. M. I. C. V. Balasubramanian M. Jawahar CVBM 36 1 0 26 Sep 2023
Interpretation on Multi-modal Visual Fusion Hao Chen Hao Zhou Yongjian Deng 39 0 0 19 Aug 2023
Identifying Interpretable Subspaces in Image Representations Neha Kalibhat S. Bhardwaj Bayan Bruss Hamed Firooz Maziar Sanjabi S. Feizi FAtt 44 26 0 20 Jul 2023
Causal Analysis for Robust Interpretability of Neural Networks Ola Ahmad Nicolas Béreux Loïc Baret V. Hashemi Freddy Lecue CML 29 3 0 15 May 2023
UFO: A unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations for CNNs V. V. Ramaswamy Sunnie S. Y. Kim Ruth C. Fong Olga Russakovsky 40 0 0 27 Mar 2023
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces Pattarawat Chormai J. Herrmann Klaus-Robert Muller G. Montavon FAtt 52 18 0 30 Dec 2022
Towards Human-Interpretable Prototypes for Visual Assessment of Image Classification Models Poulami Sinhamahapatra Lena Heidemann Maureen Monnet Karsten Roscher 47 5 0 22 Nov 2022
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction Sunnie S. Y. Kim E. A. Watkins Olga Russakovsky Ruth C. Fong Andrés Monroy-Hernández 43 108 0 02 Oct 2022
Formal Conceptual Views in Neural Networks Johannes Hirth Tom Hanika 23 2 0 27 Sep 2022
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks Tilman Raukur A. Ho Stephen Casper Dylan Hadfield-Menell AAML AI4CE 28 124 0 27 Jul 2022
Disentangling visual and written concepts in CLIP Joanna Materzyñska Antonio Torralba David Bau CoGe 34 47 0 15 Jun 2022
Post-hoc Concept Bottleneck Models Mert Yuksekgonul Maggie Wang James Zou 145 186 0 31 May 2022
Exploring Hidden Semantics in Neural Networks with Symbolic Regression Yuanzhen Luo Qiang Lu Xilei Hu Jake Luo Zhiguang Wang 13 0 0 22 Apr 2022
Deep Unlearning via Randomized Conditionally Independent Hessians Ronak R. Mehta Sourav Pal Vikas Singh Sathya Ravi MU 27 81 0 15 Apr 2022
VisCUIT: Visual Auditor for Bias in CNN Image Classifier Seongmin Lee Zijie J. Wang Judy Hoffman Duen Horng Chau 31 11 0 12 Apr 2022
Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries Haekyu Park Seongmin Lee Benjamin Hoover Austin P. Wright Omar Shaikh Rahul Duggal Nilaksh Das Kevin Li Judy Hoffman Duen Horng Chau 33 2 0 30 Mar 2022
Concept Embedding Analysis: A Review Gesina Schwalbe 34 28 0 25 Mar 2022
Sparse Subspace Clustering for Concept Discovery (SSCCD) Johanna Vielhaben Stefan Blücher Nils Strodthoff 25 6 0 11 Mar 2022
Explaining, Evaluating and Enhancing Neural Networks' Learned Representations Marco Bertolini Djork-Arné Clevert F. Montanari FAtt 16 5 0 18 Feb 2022
Deeply Explain CNN via Hierarchical Decomposition Mingg-Ming Cheng Peng-Tao Jiang Linghao Han Liang Wang Philip Torr FAtt 53 15 0 23 Jan 2022
PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability Sílvia Casacuberta Esra Suel Seth Flaxman FAtt 21 1 0 31 Dec 2021
HIVE: Evaluating the Human Interpretability of Visual Explanations Sunnie S. Y. Kim Nicole Meister V. V. Ramaswamy Ruth C. Fong Olga Russakovsky 66 114 0 06 Dec 2021
Semantic Communications With AI Tasks Yang Yang Caili Guo Fangfang Liu Chuanhong Liu Lunan Sun Qizheng Sun Jiujiu Chen 39 36 0 29 Sep 2021
Explaining Convolutional Neural Networks by Tagging Filters Anna Nguyen Daniel Hagenmayer T. Weller Michael Färber FAtt 19 0 0 20 Sep 2021
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks Haekyu Park Nilaksh Das Rahul Duggal Austin P. Wright Omar Shaikh Fred Hohman Duen Horng Chau HAI 26 25 0 29 Aug 2021
Towards Interpretable Deep Networks for Monocular Depth Estimation Zunzhi You Yi-Hsuan Tsai W. Chiu Guanbin Li FAtt 40 17 0 11 Aug 2021
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts Gesina Schwalbe Bettina Finzel XAI 34 184 0 15 May 2021
EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature Symmetry Yingqi Liu Guangyu Shen Guanhong Tao Zhenting Wang Shiqing Ma Xinming Zhang AAML 35 8 0 16 Mar 2021
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning Iro Laina Ruth C. Fong Andrea Vedaldi OCL 33 13 0 27 Oct 2020
Now You See Me (CME): Concept-based Model Extraction Dmitry Kazhdan B. Dimanov M. Jamnik Pietro Lio Adrian Weller 25 72 0 25 Oct 2020
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization Judy Borowski Roland S. Zimmermann Judith Schepers Robert Geirhos Thomas S. A. Wallis Matthias Bethge Wieland Brendel FAtt 47 7 0 23 Oct 2020
Contextual Semantic Interpretability Diego Marcos Ruth C. Fong Sylvain Lobry Rémi Flamary Nicolas Courty D. Tuia SSL 20 27 0 18 Sep 2020
Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs Matthew L. Leavitt Ari S. Morcos 58 33 0 03 Mar 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek Pascal Sturmfels Su-In Lee FAtt 30 143 0 10 Feb 2020
Understanding Deep Networks via Extremal Perturbations and Smooth Masks Ruth C. Fong Mandela Patrick Andrea Vedaldi AAML 25 411 0 18 Oct 2019
Learning Generalisable Omni-Scale Representations for Person Re-Identification Kaiyang Zhou Yongxin Yang Andrea Cavallaro Tao Xiang 30 217 0 15 Oct 2019
Understanding Neural Networks via Feature Visualization: A survey Anh Nguyen J. Yosinski Jeff Clune FAtt 19 160 0 18 Apr 2019
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations Fred Hohman Haekyu Park Caleb Robinson Duen Horng Chau FAtt 3DH HAI 24 214 0 04 Apr 2019
Explaining Neural Networks Semantically and Quantitatively Runjin Chen Hao Chen Ge Huang Jie Ren Quanshi Zhang FAtt 23 54 0 18 Dec 2018
Biased Embeddings from Wild Data: Measuring, Understanding and Removing Adam Sutton Thomas Lansdall-Welfare N. Cristianini 26 23 0 16 Jun 2018
Intriguing Properties of Randomly Weighted Networks: Generalizing While Learning Next to Nothing Amir Rosenfeld John K. Tsotsos MLT 32 51 0 02 Feb 2018
Do semantic parts emerge in Convolutional Neural Networks? Abel Gonzalez-Garcia Davide Modolo V. Ferrari 158 113 0 13 Jul 2016