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 / 604 papers shown

Title
Trustworthy AI: From Principles to Practices Bo-wen Li Peng Qi Bo Liu Shuai Di Jingen Liu Jiquan Pei Jinfeng Yi Bowen Zhou 119 356 0 04 Oct 2021
Consistent Explanations by Contrastive Learning Vipin Pillai Soroush Abbasi Koohpayegani Ashley Ouligian Dennis Fong Hamed Pirsiavash FAtt 20 21 0 01 Oct 2021
Optimising for Interpretability: Convolutional Dynamic Alignment Networks Moritz D Boehle Mario Fritz Bernt Schiele 12 2 0 27 Sep 2021
Toward a Unified Framework for Debugging Concept-based Models A. Bontempelli Fausto Giunchiglia Andrea Passerini Stefano Teso 20 4 0 23 Sep 2021
FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging Karim Lekadira Richard Osuala C. Gallin Noussair Lazrak Kaisar Kushibar ... Nickolas Papanikolaou Zohaib Salahuddin Henry C. Woodruff Philippe Lambin L. Martí-Bonmatí AI4TS 71 56 0 20 Sep 2021
Explainability Requires Interactivity Matthias Kirchler M. Graf Marius Kloft C. Lippert FAtt AAML HAI 32 1 0 16 Sep 2021
CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations Hangzhi Guo T. Nguyen A. Yadav OffRL 21 7 0 15 Sep 2021
Interactively Providing Explanations for Transformer Language Models Felix Friedrich P. Schramowski Christopher Tauchmann Kristian Kersting LRM 41 6 0 02 Sep 2021
Cross-Model Consensus of Explanations and Beyond for Image Classification Models: An Empirical Study Xuhong Li Haoyi Xiong Siyu Huang Shilei Ji Dejing Dou 22 10 0 02 Sep 2021
Spatio-Temporal Perturbations for Video Attribution Zhenqiang Li Weimin Wang Zuoyue Li Yifei Huang Yoichi Sato 16 6 0 01 Sep 2021
PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs V. Kamakshi Uday Gupta N. C. Krishnan 24 18 0 31 Aug 2021
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation Srishti Gautam Marina M.-C. Höhne Stine Hansen Robert Jenssen Michael C. Kampffmeyer 27 49 0 27 Aug 2021
ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification Dawid Rymarczyk Adam Pardyl Jaroslaw Kraus Aneta Kaczyńska M. Skomorowski Bartosz Zieliñski 17 21 0 24 Aug 2021
VAE-CE: Visual Contrastive Explanation using Disentangled VAEs Y. Poels Vlado Menkovski CoGe DRL 32 3 0 20 Aug 2021
Finding Representative Interpretations on Convolutional Neural Networks P. C. Lam Lingyang Chu Maxim Torgonskiy J. Pei Yong Zhang Lanjun Wang FAtt SSL HAI 27 6 0 13 Aug 2021
Towards Interpretable Deep Networks for Monocular Depth Estimation Zunzhi You Yi-Hsuan Tsai W. Chiu Guanbin Li FAtt 37 17 0 11 Aug 2021
Human-in-the-loop Extraction of Interpretable Concepts in Deep Learning Models Zhenge Zhao Panpan Xu C. Scheidegger Liu Ren 16 38 0 08 Aug 2021
Mixture of Linear Models Co-supervised by Deep Neural Networks Beomseok Seo Lin Lin Jia Li 9 6 0 05 Aug 2021
SONG: Self-Organizing Neural Graphs Lukasz Struski Tomasz Danel Marek Śmieja Jacek Tabor Bartosz Zieliñski 6 1 0 28 Jul 2021
LocalGLMnet: interpretable deep learning for tabular data Ronald Richman M. Wüthrich LMTD FAtt 28 27 0 23 Jul 2021
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis Bas H. M. van der Velden Hugo J. Kuijf K. Gilhuijs M. Viergever XAI 35 638 0 22 Jul 2021
Towards Privacy-preserving Explanations in Medical Image Analysis H. Montenegro W. Silva Jaime S. Cardoso 6 7 0 20 Jul 2021
Interpretable Mammographic Image Classification using Case-Based Reasoning and Deep Learning A. Barnett F. Schwartz Chaofan Tao Chaofan Chen Yinhao Ren J. Lo Cynthia Rudin 64 21 0 12 Jul 2021
Interpretable Compositional Convolutional Neural Networks Wen Shen Zhihua Wei Shikun Huang Binbin Zhang Jiaqi Fan Ping Zhao Quanshi Zhang FAtt 6 34 0 09 Jul 2021
When and How to Fool Explainable Models (and Humans) with Adversarial Examples Jon Vadillo Roberto Santana Jose A. Lozano SILM AAML 36 12 0 05 Jul 2021
Explainable Diabetic Retinopathy Detection and Retinal Image Generation Yuhao Niu Lin Gu Yitian Zhao Feng Lu MedIm 22 58 0 01 Jul 2021
Towards Fully Interpretable Deep Neural Networks: Are We There Yet? Sandareka Wickramanayake W. Hsu M. Lee FaML AI4CE 13 3 0 24 Jun 2021
Feature Attributions and Counterfactual Explanations Can Be Manipulated Dylan Slack Sophie Hilgard Sameer Singh Himabindu Lakkaraju FAtt 14 6 0 23 Jun 2021
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning Yang Liu Sujay Khandagale Colin White W. Neiswanger 37 65 0 23 Jun 2021
Visual Probing: Cognitive Framework for Explaining Self-Supervised Image Representations Witold Oleszkiewicz Dominika Basaj Igor Sieradzki Michal Górszczak Barbara Rychalska K. Lewandowska Tomasz Trzciñski Bartosz Zieliñski SSL 37 3 0 21 Jun 2021
It's FLAN time! Summing feature-wise latent representations for interpretability An-phi Nguyen María Rodríguez Martínez FAtt 13 0 0 18 Jun 2021
Model-Based Counterfactual Synthesizer for Interpretation Fan Yang Sahan Suresh Alva Jiahao Chen X. Hu 17 30 0 16 Jun 2021
Input Invex Neural Network Suman Sapkota Binod Bhattarai 17 4 0 16 Jun 2021
Best of both worlds: local and global explanations with human-understandable concepts Jessica Schrouff Sebastien Baur Shaobo Hou Diana Mincu Eric Loreaux Ralph Blanes James Wexler Alan Karthikesalingam Been Kim FAtt 26 27 0 16 Jun 2021
Keep CALM and Improve Visual Feature Attribution Jae Myung Kim Junsuk Choe Zeynep Akata Seong Joon Oh FAtt 344 20 0 15 Jun 2021
Neural Networks for Partially Linear Quantile Regression Qixian Zhong Jane-ling Wang 18 13 0 11 Jun 2021
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of Attention T. Gomez Suiyi Ling Thomas Fréour Harold Mouchère 12 5 0 04 Jun 2021
Independent Prototype Propagation for Zero-Shot Compositionality Frank Ruis Gertjan J. Burghouts Doina Bucur 14 52 0 01 Jun 2021
Memory Wrap: a Data-Efficient and Interpretable Extension to Image Classification Models B. La Rosa Roberto Capobianco Daniele Nardi VLM 27 9 0 01 Jun 2021
A Methodology for Exploring Deep Convolutional Features in Relation to Hand-Crafted Features with an Application to Music Audio Modeling Anna K. Yanchenko Mohammadreza Soltani Robert J. Ravier S. Mukherjee Vahid Tarokh 14 2 0 31 May 2021
The effectiveness of feature attribution methods and its correlation with automatic evaluation scores Giang Nguyen Daeyoung Kim Anh Totti Nguyen FAtt 18 86 0 31 May 2021
Bounded logit attention: Learning to explain image classifiers Thomas Baumhauer D. Slijepcevic Matthias Zeppelzauer FAtt 14 2 0 31 May 2021
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts Gesina Schwalbe Bettina Finzel XAI 29 183 0 15 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 22 63 0 05 May 2021
Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization M. Afnan Cynthia Rudin Vincent Conitzer J. Savulescu Abhishek Mishra Yanhe Liu M. Afnan SyDa 25 17 0 30 Apr 2021
Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box Vulnerabilities Julia Rosenzweig Joachim Sicking Sebastian Houben Michael Mock Maram Akila AAML 29 3 0 22 Apr 2021
Improving Attribution Methods by Learning Submodular Functions Piyushi Manupriya Tarun Ram Menta S. Jagarlapudi V. Balasubramanian TDI 24 6 0 19 Apr 2021
NICE: An Algorithm for Nearest Instance Counterfactual Explanations Dieter Brughmans Pieter Leyman David Martens 35 63 0 15 Apr 2021
Enhancing Deep Neural Network Saliency Visualizations with Gradual Extrapolation Tomasz Szandała FAtt 16 4 0 11 Apr 2021
Explainability-aided Domain Generalization for Image Classification Robin M. Schmidt FAtt OOD 24 1 0 05 Apr 2021