Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

30 November 2017

Justin Gilmer

Papers citing "Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)"

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Title
Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction Shauli Ravfogel Grusha Prasad Tal Linzen Yoav Goldberg 20 57 0 14 May 2021
Verification of Size Invariance in DNN Activations using Concept Embeddings Gesina Schwalbe 3DPC 17 8 0 14 May 2021
XAI Handbook: Towards a Unified Framework for Explainable AI Sebastián M. Palacio Adriano Lucieri Mohsin Munir Jörn Hees Sheraz Ahmed Andreas Dengel 23 32 0 14 May 2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks Eric Wong Shibani Santurkar A. Madry FAtt 20 88 0 11 May 2021
Rationalization through Concepts Diego Antognini Boi Faltings FAtt 24 19 0 11 May 2021
e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks Maxime Kayser Oana-Maria Camburu Leonard Salewski Cornelius Emde Virginie Do Zeynep Akata Thomas Lukasiewicz VLM 26 100 0 08 May 2021
Explainable Autonomous Robots: A Survey and Perspective Tatsuya Sakai Takayuki Nagai 20 67 0 06 May 2021
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks Adrian Hoffmann Claudio Fanconi Rahul Rade Jonas Köhler 14 63 0 05 May 2021
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety Sebastian Houben Stephanie Abrecht Maram Akila Andreas Bär Felix Brockherde ... Serin Varghese Michael Weber Sebastian J. Wirkert Tim Wirtz Matthias Woehrle AAML 13 58 0 29 Apr 2021
Explaining in Style: Training a GAN to explain a classifier in StyleSpace Oran Lang Yossi Gandelsman Michal Yarom Yoav Wald G. Elidan ... William T. Freeman Phillip Isola Amir Globerson Michal Irani Inbar Mosseri GAN 40 152 0 27 Apr 2021
Exploiting Explanations for Model Inversion Attacks Xu Zhao Wencan Zhang Xiao Xiao Brian Y. Lim MIACV 21 82 0 26 Apr 2021
Weakly Supervised Multi-task Learning for Concept-based Explainability Catarina Belém Vladimir Balayan Pedro Saleiro P. Bizarro 78 10 0 26 Apr 2021
Towards Human-Understandable Visual Explanations:Imperceptible High-frequency Cues Can Better Be Removed Kaili Wang José Oramas Tinne Tuytelaars AAML 11 2 0 16 Apr 2021
NICE: An Algorithm for Nearest Instance Counterfactual Explanations Dieter Brughmans Pieter Leyman David Martens 33 63 0 15 Apr 2021
Do Deep Neural Networks Forget Facial Action Units? -- Exploring the Effects of Transfer Learning in Health Related Facial Expression Recognition Pooja Prajod Dominik Schiller Tobias Huber Elisabeth André CVBM 11 8 0 15 Apr 2021
An Interpretability Illusion for BERT Tolga Bolukbasi Adam Pearce Ann Yuan Andy Coenen Emily Reif Fernanda Viégas Martin Wattenberg MILM FAtt 32 68 0 14 Apr 2021
What Makes a Scientific Paper be Accepted for Publication? Panagiotis Fytas Georgios Rizos Lucia Specia 11 10 0 14 Apr 2021
Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches Dmitry Kazhdan B. Dimanov Helena Andrés-Terré M. Jamnik Pietro Lió Adrian Weller CoGe DRL 12 22 0 14 Apr 2021
Deep Interpretable Models of Theory of Mind Ini Oguntola Dana Hughes Katia P. Sycara HAI 25 23 0 07 Apr 2021
Robust Semantic Interpretability: Revisiting Concept Activation Vectors J. Pfau A. Young Jerome Wei Maria L. Wei Michael J. Keiser FAtt 31 14 0 06 Apr 2021
Neural Response Interpretation through the Lens of Critical Pathways Ashkan Khakzar Soroosh Baselizadeh Saurabh Khanduja Christian Rupprecht Seong Tae Kim Nassir Navab 29 32 0 31 Mar 2021
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice David S. Watson Limor Gultchin Ankur Taly Luciano Floridi 20 62 0 27 Mar 2021
Robust Models Are More Interpretable Because Attributions Look Normal Zifan Wang Matt Fredrikson Anupam Datta OOD FAtt 30 25 0 20 Mar 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 59 651 0 20 Mar 2021
Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond Xuhong Li Haoyi Xiong Xingjian Li Xuanyu Wu Xiao Zhang Ji Liu Jiang Bian Dejing Dou AAML FaML XAI HAI 18 315 0 19 Mar 2021
XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations Eunji Kim Siwon Kim Minji Seo Sungroh Yoon ViT FAtt 16 114 0 19 Mar 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 X. Zhang AAML 22 8 0 16 Mar 2021
Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset Adrianna Janik J. Dodd Georgiana Ifrim Kris Sankaran Kathleen M. Curran 25 26 0 15 Mar 2021
CACTUS: Detecting and Resolving Conflicts in Objective Functions Subhajit Das Alex Endert 19 0 0 13 Mar 2021
Detecting Spurious Correlations with Sanity Tests for Artificial Intelligence Guided Radiology Systems U. Mahmood Robik Shrestha D. Bates L. Mannelli G. Corrias Y. Erdi Christopher Kanan 16 16 0 04 Mar 2021
Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction Aniruddh Raghu John Guttag K. Young E. Pomerantsev Adrian V. Dalca Collin M. Stultz 13 9 0 04 Mar 2021
Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis Adriano Lucieri Andreas Dengel Sheraz Ahmed 9 7 0 02 Mar 2021
Contrastive Explanations for Model Interpretability Alon Jacovi Swabha Swayamdipta Shauli Ravfogel Yanai Elazar Yejin Choi Yoav Goldberg 35 95 0 02 Mar 2021
Interpretable Artificial Intelligence through the Lens of Feature Interaction Michael Tsang James Enouen Yan Liu FAtt 12 7 0 01 Mar 2021
Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence Atoosa Kasirzadeh 27 24 0 01 Mar 2021
Benchmarking and Survey of Explanation Methods for Black Box Models F. Bodria F. Giannotti Riccardo Guidotti Francesca Naretto D. Pedreschi S. Rinzivillo XAI 33 220 0 25 Feb 2021
Interpretability in Contact-Rich Manipulation via Kinodynamic Images Ioanna Mitsioni Joonatan Mänttäri Y. Karayiannidis John Folkesson Danica Kragic FAtt 11 3 0 23 Feb 2021
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations Sushant Agarwal S. Jabbari Chirag Agarwal Sohini Upadhyay Zhiwei Steven Wu Himabindu Lakkaraju FAtt AAML 10 60 0 21 Feb 2021
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs Harini Suresh Kathleen M. Lewis John Guttag Arvind Satyanarayan FAtt 34 25 0 17 Feb 2021
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset Chuizheng Meng Loc Trinh Nan Xu Yan Liu 24 30 0 12 Feb 2021
PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification Dominique Mercier Andreas Dengel Sheraz Ahmed AI4TS 12 5 0 11 Feb 2021
EUCA: the End-User-Centered Explainable AI Framework Weina Jin Jianyu Fan D. Gromala Philippe Pasquier Ghassan Hamarneh 40 24 0 04 Feb 2021
Evaluating the Interpretability of Generative Models by Interactive Reconstruction A. Ross Nina Chen Elisa Zhao Hang Elena L. Glassman Finale Doshi-Velez 103 49 0 02 Feb 2021
Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models Soya Park A. Wang B. Kawas Q. V. Liao David Piorkowski Marina Danilevsky 62 55 0 29 Jan 2021
Explaining Natural Language Processing Classifiers with Occlusion and Language Modeling David Harbecke AAML 27 2 0 28 Jan 2021
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs Harini Suresh Steven R. Gomez K. Nam Arvind Satyanarayan 34 126 0 24 Jan 2021
Show or Suppress? Managing Input Uncertainty in Machine Learning Model Explanations Danding Wang Wencan Zhang Brian Y. Lim FAtt 19 22 0 23 Jan 2021
Explainable Artificial Intelligence Approaches: A Survey Sheikh Rabiul Islam W. Eberle S. Ghafoor Mohiuddin Ahmed XAI 29 103 0 23 Jan 2021
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations Sérgio Jesus Catarina Belém Vladimir Balayan João Bento Pedro Saleiro P. Bizarro João Gama 136 119 0 21 Jan 2021
Explaining the Black-box Smoothly- A Counterfactual Approach Junyu Chen Yong Du Yufan He W. Paul Segars Ye Li MedIm FAtt 65 83 0 11 Jan 2021