Counterfactual Visual Explanations

16 April 2019

Ziyan Wu

Devi Parikh

Papers citing "Counterfactual Visual Explanations"

50 / 158 papers shown

Title
Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutions Eoin Delaney Derek Greene Mark T. Keane 38 24 0 20 Jul 2021
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis Linyi Yang Jiazheng Li Padraig Cunningham Yue Zhang Barry Smyth Ruihai Dong 27 47 0 29 Jun 2021
How Well do Feature Visualizations Support Causal Understanding of CNN Activations? Roland S. Zimmermann Judy Borowski Robert Geirhos Matthias Bethge Thomas S. A. Wallis Wieland Brendel FAtt 49 31 0 23 Jun 2021
Keep CALM and Improve Visual Feature Attribution Jae Myung Kim Junsuk Choe Zeynep Akata Seong Joon Oh FAtt 350 20 0 15 Jun 2021
Conterfactual Generative Zero-Shot Semantic Segmentation Feihong Shen Jun Liu Ping Hu BDL 39 9 0 11 Jun 2021
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 40 40 0 07 Jun 2021
A Review on Explainability in Multimodal Deep Neural Nets Gargi Joshi Rahee Walambe K. Kotecha 34 140 0 17 May 2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks Eric Wong Shibani Santurkar Aleksander Madry FAtt 22 88 0 11 May 2021
Two4Two: Evaluating Interpretable Machine Learning - A Synthetic Dataset For Controlled Experiments M. Schuessler Philipp Weiß Leon Sixt 43 3 0 06 May 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 50 152 0 27 Apr 2021
Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis Samuele Poppi Marcella Cornia Lorenzo Baraldi Rita Cucchiara FAtt 131 33 0 20 Apr 2021
Contrastive Reasoning in Neural Networks Mohit Prabhushankar Ghassan AlRegib 25 10 0 23 Mar 2021
Robust Models Are More Interpretable Because Attributions Look Normal Zifan Wang Matt Fredrikson Anupam Datta OOD FAtt 35 25 0 20 Mar 2021
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations Pau Rodríguez López Massimo Caccia Alexandre Lacoste L. Zamparo I. Laradji Laurent Charlin David Vazquez AAML 42 55 0 18 Mar 2021
Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning Mark Hamilton Scott M. Lundberg Lei Zhang Stephanie Fu William T. Freeman FAtt 35 10 0 28 Feb 2021
If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques Mark T. Keane Eoin M. Kenny Eoin Delaney Barry Smyth CML 32 146 0 26 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 45 25 0 17 Feb 2021
Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching Scott Cheng-Hsin Yang Wai Keen Vong Ravi B. Sojitra Tomas Folke Patrick Shafto 24 42 0 07 Feb 2021
EUCA: the End-User-Centered Explainable AI Framework Weina Jin Jianyu Fan D. Gromala Philippe Pasquier Ghassan Hamarneh 42 24 0 04 Feb 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
Explainability of deep vision-based autonomous driving systems: Review and challenges Éloi Zablocki H. Ben-younes P. Pérez Matthieu Cord XAI 53 170 0 13 Jan 2021
Explaining the Black-box Smoothly- A Counterfactual Approach Junyu Chen Yong Du Yufan He W. Paul Segars Ye Li MedIm FAtt 70 100 0 11 Jan 2021
Explaining NLP Models via Minimal Contrastive Editing (MiCE) Alexis Ross Ana Marasović Matthew E. Peters 43 121 0 27 Dec 2020
GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learning Silvan Mertes Tobias Huber Katharina Weitz Alexander Heimerl Elisabeth André GAN AAML MedIm 39 69 0 22 Dec 2020
Understanding Learned Reward Functions Eric J. Michaud Adam Gleave Stuart J. Russell XAI OffRL 30 33 0 10 Dec 2020
Understanding Failures of Deep Networks via Robust Feature Extraction Sahil Singla Besmira Nushi S. Shah Ece Kamar Eric Horvitz FAtt 28 83 0 03 Dec 2020
Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things Jing Zhang Dacheng Tao 45 463 0 17 Nov 2020
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review Sahil Verma Varich Boonsanong Minh Hoang Keegan E. Hines John P. Dickerson Chirag Shah CML 28 164 0 20 Oct 2020
Counterfactual Variable Control for Robust and Interpretable Question Answering S. Yu Yulei Niu Shuohang Wang Jing Jiang Qianru Sun AAML OOD 44 9 0 12 Oct 2020
Instance-based Counterfactual Explanations for Time Series Classification Eoin Delaney Derek Greene Mark T. Keane CML AI4TS 24 89 0 28 Sep 2020
Counterfactual Explanation and Causal Inference in Service of Robustness in Robot Control Simón C. Smith S. Ramamoorthy 26 13 0 18 Sep 2020
Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals Saloni Dash V. Balasubramanian Amit Sharma CML 27 65 0 17 Sep 2020
SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition Liangzhi Li Bowen Wang Manisha Verma Yuta Nakashima R. Kawasaki Hajime Nagahara OCL 23 49 0 14 Sep 2020
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples Timo Freiesleben GAN 46 62 0 11 Sep 2020
Counterfactual Explanation Based on Gradual Construction for Deep Networks Hong G Jung Sin-Han Kang Hee-Dong Kim Dong-Ok Won Seong-Whan Lee OOD FAtt 25 22 0 05 Aug 2020
Contrastive Explanations in Neural Networks Mohit Prabhushankar Gukyeong Kwon Dogancan Temel Ghassan AlRegib FAtt 18 33 0 01 Aug 2020
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models Lifu Tu Garima Lalwani Spandana Gella He He LRM 33 184 0 14 Jul 2020
Fast Real-time Counterfactual Explanations Yunxia Zhao 17 15 0 11 Jul 2020
Counterfactual explanation of machine learning survival models M. Kovalev Lev V. Utkin CML OffRL 37 19 0 26 Jun 2020
Counterfactual VQA: A Cause-Effect Look at Language Bias Yulei Niu Kaihua Tang Hanwang Zhang Zhiwu Lu Xiansheng Hua Ji-Rong Wen CML 61 395 0 08 Jun 2020
CausaLM: Causal Model Explanation Through Counterfactual Language Models Amir Feder Nadav Oved Uri Shalit Roi Reichart CML LRM 56 157 0 27 May 2020
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds M. Kovalev Lev V. Utkin AAML 43 31 0 05 May 2020
Adversarial Attacks and Defenses: An Interpretation Perspective Ninghao Liu Mengnan Du Ruocheng Guo Huan Liu Xia Hu AAML 31 8 0 23 Apr 2020
SCOUT: Self-aware Discriminant Counterfactual Explanations Pei Wang Nuno Vasconcelos FAtt 30 81 0 16 Apr 2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability Maximilian Augustin Alexander Meinke Matthias Hein OOD 80 99 0 20 Mar 2020
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation Raha Moraffah Mansooreh Karami Ruocheng Guo A. Raglin Huan Liu CML ELM XAI 32 213 0 09 Mar 2020
Evaluating Weakly Supervised Object Localization Methods Right Junsuk Choe Seong Joon Oh Seungho Lee Sanghyuk Chun Zeynep Akata Hyunjung Shim WSOL 303 186 0 21 Jan 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 45 301 0 08 Jan 2020
Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia D. Ravì Stefano B. Blumberg S. Ingala F. Barkhof Daniel C. Alexander N. Oxtoby MedIm 29 31 0 03 Dec 2019
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling Tsu-Jui Fu Xinze Wang Matthew F. Peterson Scott T. Grafton Miguel P. Eckstein William Yang Wang 57 41 0 17 Nov 2019