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Part-prototype Networks (ProtoPNets) are concept-based classifiers designed to achieve the same performance as black-box models without compromising transparency.
Bimcv covid-19+: a large annotated dataset of rx and ct images from covid-19 patients
Maria de la Iglesia Vayá, Jose Manuel Saborit, Joaquim Angel Montell, Antonio Pertusa, Aurelia Bustos, Miguel Cazorla, Joaquin Galant, Xavier Barber, Domingo Orozco-Beltrán, Francisco García-García, Marisa Caparrós, Germán González, and Jose María Salinas · 2006
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Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko · 2007
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Confounding: What it is and how to deal with it
K.J. Jager, C. Zoccali, A. MacLeod, and F.W. Dekker · 2008
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How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller · 2010
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The Caltech-UCSD Birds-200-2011 Dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Principles of explanatory debugging to personalize interactive machine learning
Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf · 2015
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“Why should I trust you?” Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Right for the right reasons: training differentiable models by constraining their explanations
Andrew Slavin Ross, Michael C Hughes, and Finale Doshi-Velez · 2017
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Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald Summers · 2017
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Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and Tommi S Jaakkola · 2018
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Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions
Oscar Li, Hao Liu, Chaofan Chen, and Cynthia Rudin · 2018
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Statistical challenges in “big data” human neuroimaging
Stephen M. Smith and Thomas E. Nichols · 2018
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URL https://github.com/cfchen-duke/ProtoPNet
Protopnet source code, 2019 · 2019
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This looks like that: Deep learning for interpretable image recognition
Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K Su · 2019
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Explanations can be manipulated and geometry is to blame
Ann-Kathrin Dombrowski, Maximillian Alber, Christopher Anders, Marcel Ackermann, Klaus-Robert Müller, and Pan Kessel · 2019
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High-performance medicine: the convergence of human and artificial intelligence
Topol EJ · 2019
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Interpretable image recognition with hierarchical prototypes
Peter Hase, Chaofan Chen, Oscar Li, and Cynthia Rudin · 2019
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Unmasking clever hans predictors and assessing what machines really learn
Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2019
Earlier work this paper cites.
Interpretability beyond classification output: Semantic Bottleneck Networks
Max Losch, Mario Fritz, and Bernt Schiele · 2019
Cited alongside, same era.
Interpretable and steerable sequence learning via prototypes
Yao Ming, Panpan Xu, Huamin Qu, and Liu Ren · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded
Ramprasaath R Selvaraju, Stefan Lee, Yilin Shen, Hongxia Jin, Shalini Ghosh, Larry Heck, Dhruv Batra, and Devi Parikh · 2019
Cited alongside, same era.
Toward faithful explanatory active learning with self-explainable neural nets
Stefano Teso · 2019
Cited alongside, same era.
Noise or signal: The role of image backgrounds in object recognition
Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 2020
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A case-based interpretable deep learning model for classification of mass lesions in digital mammography
Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y Lo, and Cynthia Rudin · 2021
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Toward a Unified Framework for Debugging Gray-box Models
Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini, and Stefano Teso · 2021
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Toward faithful case-based reasoning through learning prototypes in a nearest neighbor-friendly space
Seyed Omid Davoudi and Majid Komeili · 2021
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Ai for radiographic covid-19 detection selects shortcuts over signal
Alex J DeGrave, Joseph D Janizek, and Su-In Lee · 2021
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Explanatory interactive machine learning
Stefano Teso and Kristian Kersting · 2019
Cited alongside, same era.
Padchest: A large chest x-ray image dataset with multi-label annotated reports
Aurelia Bustos, Antonio Pertusa, Jose-Maria Salinas, and Maria de la Iglesia-Vayá · 2020
Cited alongside, same era.
Concept whitening for interpretable image recognition
Zhi Chen, Yijie Bei, and Cynthia Rudin · 2020
Cited alongside, same era.
Covid-19 image data collection
Joseph Paul Cohen, Paul Morrison, and Lan Dao · 2020
Cited alongside, same era.
Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
Cited alongside, same era.
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?
Peter Hase and Mohit Bansal · 2020
Cited alongside, same era.
Concept bottleneck models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang · 2020
Cited alongside, same era.
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
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Adrian Hoffmann, Claudio Fanconi, Rahul Rade, and Jonas Kohler · 2021
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Sparrow: Semantically coherent prototypes for image classification
Stefan Kraft, Klaus Broelemann, Andreas Theissler, Gjergji Kasneci, GER Esslingen am Neckar, SCHUFA Holding AG, GER Wiesbaden, and GER Aalen · 2021
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Explanation-based human debugging of nlp models: A survey
Piyawat Lertvittayakumjorn and Francesca Toni · 2021
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Neural prototype trees for interpretable fine-grained image recognition
Meike Nauta, Ron van Bree, and Christin Seifert · 2021
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ProtoPShare: Prototypical Parts Sharing for Similarity Discovery in Interpretable Image Classification
Dawid Rymarczyk, Łukasz Struski, Jacek Tabor, and Bartosz Zieliński · 2021
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Interactively Generating Explanations for Transformer Language Models
Patrick Schramowski, Felix Friedrich, Christopher Tauchmann, and Kristian Kersting · 2021
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Right for Better Reasons: Training Differentiable Models by Constraining their Influence Function
Xiaoting Shao, Arseny Skryagin, P Schramowski, W Stammer, and Kristian Kersting · 2021
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Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations
Wolfgang Stammer, Patrick Schramowski, and Kristian Kersting · 2021
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Interactive Label Cleaning with Example-based Explanations
Stefano Teso, Andrea Bontempelli, Fausto Giunchiglia, and Andrea Passerini · 2021
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Saliency is a possible red herring when diagnosing poor generalization
Joseph D Viviano, Becks Simpson, Francis Dutil, Yoshua Bengio, and Joseph Paul Cohen · 2021
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HILDIF: Interactive Debugging of NLI Models Using Influence Functions
Hugo Zylberajch, Piyawat Lertvittayakumjorn, and Francesca Toni · 2021
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Cub-200-2011 segmentations, apr 2022
Farrell · 2022
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A typology to explore and guide explanatory interactive machine learning
Felix Friedrich, Wolfgang Stammer, Patrick Schramowski, and Kristian Kersting · 2022
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Glancenets: Interpretabile, leak-proof concept-based models
Emanuele Marconato, Andrea Passerini, and Stefano Teso · 2022
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Leveraging explanations in interactive machine learning: An overview
Stefano Teso, Öznur Alkan, Wolfang Stammer, and Elizabeth Daly · 2022
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