This Looks Like That: Deep Learning for Interpretable Image Recognition

27 June 2018

Papers citing "This Looks Like That: Deep Learning for Interpretable Image Recognition"

50 / 602 papers shown

Title
Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors Ruihan Zhang Prashan Madumal Tim Miller Krista A. Ehinger Benjamin I. P. Rubinstein FAtt 19 94 0 27 Jun 2020
Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning Johnathon Shook Tryambak Gangopadhyay Linjian Wu Baskar Ganapathysubramanian S. Sarkar Asheesh K. Singh 24 131 0 24 Jun 2020
Towards Robust Fine-grained Recognition by Maximal Separation of Discriminative Features K. K. Nakka Mathieu Salzmann AAML 22 6 0 10 Jun 2020
Evaluation of Similarity-based Explanations Kazuaki Hanawa Sho Yokoi Satoshi Hara Kentaro Inui XAI 6 2 0 08 Jun 2020
A Generic and Model-Agnostic Exemplar Synthetization Framework for Explainable AI Antonio Bărbălău Adrian Cosma Radu Tudor Ionescu Marius Popescu 9 9 0 06 Jun 2020
Kernel Self-Attention in Deep Multiple Instance Learning Dawid Rymarczyk Adriana Borowa Jacek Tabor Bartosz Zieliñski SSL 14 5 0 25 May 2020
Focus Longer to See Better:Recursively Refined Attention for Fine-Grained Image Classification Prateek Shroff Tianlong Chen Yunchao Wei Zhangyang Wang 6 12 0 22 May 2020
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges D. Charte F. Charte M. J. D. Jesus Francisco Herrera SSL OOD 13 51 0 21 May 2020
Interpretable and Accurate Fine-grained Recognition via Region Grouping Zixuan Huang Yin Li 12 138 0 21 May 2020
P2ExNet: Patch-based Prototype Explanation Network Dominique Mercier Andreas Dengel Sheraz Ahmed 8 3 0 05 May 2020
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? Peter Hase Joey Tianyi Zhou FAtt 29 298 0 04 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 38 370 0 30 Apr 2020
Semi-Lexical Languages -- A Formal Basis for Unifying Machine Learning and Symbolic Reasoning in Computer Vision Briti Gangopadhyay S. Hazra P. Dasgupta NAI 23 0 0 25 Apr 2020
Adversarial Attacks and Defenses: An Interpretation Perspective Ninghao Liu Mengnan Du Ruocheng Guo Huan Liu Xia Hu AAML 26 8 0 23 Apr 2020
CoInGP: Convolutional Inpainting with Genetic Programming D. Jakobović L. Manzoni L. Mariot S. Picek Mauro Castelli 8 2 0 23 Apr 2020
Games for Fairness and Interpretability Eric Chu Nabeel Gillani S. Makini FaML 4 4 0 20 Apr 2020
Neural encoding and interpretation for high-level visual cortices based on fMRI using image caption features Kai Qiao Chi Zhang Jian Chen Linyuan Wang Li Tong Bin Yan 14 3 0 26 Mar 2020
Using Wavelets to Analyze Similarities in Image-Classification Datasets Roozbeh Yousefzadeh 21 0 0 24 Feb 2020
Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example Serena Booth Yilun Zhou Ankit J. Shah J. Shah BDL 12 2 0 19 Feb 2020
Explainable Deep Modeling of Tabular Data using TableGraphNet G. Terejanu Jawad Chowdhury Rezaur Rashid Asif J. Chowdhury LMTD FAtt 14 3 0 12 Feb 2020
Self-explaining AI as an alternative to interpretable AI Daniel C. Elton 6 56 0 12 Feb 2020
Concept Whitening for Interpretable Image Recognition Zhi Chen Yijie Bei Cynthia Rudin FAtt 31 314 0 05 Feb 2020
DANCE: Enhancing saliency maps using decoys Y. Lu Wenbo Guo Xinyu Xing William Stafford Noble AAML 32 14 0 03 Feb 2020
Localizing Interpretable Multi-scale informative Patches Derived from Media Classification Task Chuanguang Yang Zhulin An Xiaolong Hu Hui Zhu Yongjun Xu 12 0 0 31 Jan 2020
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space Riccardo Guidotti A. Monreale Stan Matwin D. Pedreschi FAtt 8 67 0 27 Jan 2020
Making deep neural networks right for the right scientific reasons by interacting with their explanations P. Schramowski Wolfgang Stammer Stefano Teso Anna Brugger Xiaoting Shao Hans-Georg Luigs Anne-Katrin Mahlein Kristian Kersting 32 207 0 15 Jan 2020
Universal Differential Equations for Scientific Machine Learning Christopher Rackauckas Yingbo Ma Julius Martensen Collin Warner K. Zubov R. Supekar Dominic J. Skinner Ali Ramadhan Alan Edelman AI4CE 33 569 0 13 Jan 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 38 300 0 08 Jan 2020
Deep neural network models for computational histopathology: A survey C. Srinidhi Ozan Ciga Anne L. Martel AI4CE 30 563 0 28 Dec 2019
Balancing the Tradeoff Between Clustering Value and Interpretability Sandhya Saisubramanian Sainyam Galhotra S. Zilberstein 16 40 0 17 Dec 2019
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan Ö. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 122 297 0 17 Oct 2019
Interpretable and Steerable Sequence Learning via Prototypes Yao Ming Panpan Xu Huamin Qu Liu Ren AI4TS 10 138 0 23 Jul 2019
Interpretable Image Recognition with Hierarchical Prototypes Peter Hase Chaofan Chen Oscar Li Cynthia Rudin VLM 17 109 0 25 Jun 2019
Understanding artificial intelligence ethics and safety David Leslie FaML AI4TS 22 345 0 11 Jun 2019
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis Cynthia Rudin David Carlson HAI 17 34 0 04 Jun 2019
Analyzing the Interpretability Robustness of Self-Explaining Models Haizhong Zheng Earlence Fernandes A. Prakash AAML LRM 16 7 0 27 May 2019
The Twin-System Approach as One Generic Solution for XAI: An Overview of ANN-CBR Twins for Explaining Deep Learning Mark T. Keane Eoin M. Kenny 24 13 0 20 May 2019
How Case Based Reasoning Explained Neural Networks: An XAI Survey of Post-Hoc Explanation-by-Example in ANN-CBR Twins Mark T. Keane Eoin M. Kenny 13 82 0 17 May 2019
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model Tong Wang Qihang Lin 35 19 0 10 May 2019
AI Enabling Technologies: A Survey V. Gadepally Justin A. Goodwin J. Kepner Albert Reuther Hayley Reynolds S. Samsi Jonathan Su David Martinez 27 24 0 08 May 2019
Explaining Deep Classification of Time-Series Data with Learned Prototypes Alan H. Gee Diego Garcia-Olano Joydeep Ghosh D. Paydarfar AI4TS 16 66 0 18 Apr 2019
A Categorisation of Post-hoc Explanations for Predictive Models John Mitros Brian Mac Namee XAI CML 16 0 0 04 Apr 2019
Interpreting Neural Networks Using Flip Points Roozbeh Yousefzadeh D. O’Leary AAML FAtt 22 17 0 21 Mar 2019
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet Wieland Brendel Matthias Bethge SSL FAtt 26 557 0 20 Mar 2019
ProtoAttend: Attention-Based Prototypical Learning Sercan Ö. Arik Tomas Pfister 19 19 0 17 Feb 2019
Explaining Explanations to Society Leilani H. Gilpin Cecilia Testart Nathaniel Fruchter Julius Adebayo XAI 24 34 0 19 Jan 2019
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead Cynthia Rudin ELM FaML 14 217 0 26 Nov 2018
Embedding Deep Networks into Visual Explanations Zhongang Qi Saeed Khorram Fuxin Li 24 27 0 15 Sep 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Learning Optimized Risk Scores Berk Ustun Cynthia Rudin 17 82 0 01 Oct 2016