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Concept Bottleneck Models (CBMs) map the inputs onto a set of interpretable concepts (``the bottleneck'') and use the concepts to make predictions.
A sensitivity and specificity analysis of the surface microscopy features of invasive melanoma
SW Menzies, C Ingvar, and WH McCarthy · 1996
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Nonparametric and semiparametric models , volume 1
Wolfgang Härdle, Marlene Müller, Stefan Sperlich, and Axel Werwatz · 2004
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Attribute and simile classifiers for face verification
Neeraj Kumar, Alexander C Berg, Peter N Belhumeur, and Shree K Nayar · 2009
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Learning to detect unseen object classes by between-class attribute transfer
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
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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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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi · 2017
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Coco-stuff: Thing and stuff classes in context
Holger Caesar, Jasper Uijlings, and Vittorio Ferrari · 2018
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Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks
Ruth Fong and Andrea Vedaldi · 2018
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Seven-point checklist and skin lesion classification using multitask multimodal neural nets
Jeremy Kawahara, Sara Daneshvar, Giuseppe Argenziano, and Ghassan Hamarneh · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al · 2018
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The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Philipp Tschandl, Cliff Rosendahl, and Harald Kittler · 2018
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Towards automatic concept-based explanations
Amirata Ghorbani, James Wexler, James Y Zou, and Been Kim · 2019
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Explaining classifiers with causal concept effect (cace)
Yash Goyal, Amir Feder, Uri Shalit, and Been Kim · 2019
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Cocox: Generating conceptual and counterfactual explanations via fault-lines
Arjun Akula, Shuai Wang, and Song-Chun Zhu · 2020
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Rewriting a deep generative model
David Bau, Steven Liu, Tongzhou Wang, Jun-Yan Zhu, and Antonio Torralba · 2020
Cited alongside, same era.
Concept whitening for interpretable image recognition
Zhi Chen, Yijie Bei, and Cynthia Rudin · 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.
Find: Human-in-the-loop debugging deep text classifiers
Piyawat Lertvittayakumjorn, Lucia Specia, and Francesca Toni · 2020
Cited alongside, same era.
On interpretability of deep learning based skin lesion classifiers using concept activation vectors
Causalm: Causal model explanation through counterfactual language models
Amir Feder, Nadav Oved, Uri Shalit, and Roi Reichart · 2021
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Dissect: Disentangled simultaneous explanations via concept traversals
Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, and Rosalind W Picard · 2021
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Do language models have beliefs? methods for detecting, updating, and visualizing model beliefs
Peter Hase, Mona Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, and Srinivasan Iyer · 2021
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Explaining in style: Training a gan to explain a classifier in stylespace
Oran Lang, Yossi Gandelsman, Michal Yarom, Yoav Wald, Gal Elidan, Avinatan Hassidim, William T Freeman, Phillip Isola, Amir Globerson, Michal Irani, et al · 2021
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Adriano Lucieri, Muhammad Naseer Bajwa, Stephan Alexander Braun, Muhammad Imran Malik, Andreas Dengel, and Sheraz Ahmed · 2020
Cited alongside, same era.
Don’t judge an object by its context: learning to overcome contextual bias
Krishna Kumar Singh, Dhruv Mahajan, Kristen Grauman, Yong Jae Lee, Matt Feiszli, and Deepti Ghadiyaram · 2020
Cited alongside, same era.
Editable neural networks
Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitry Pyrkin, Sergei Popov, and Artem Babenko · 2020
Cited alongside, same era.
On completeness-aware concept-based explanations in deep neural networks
Chih-Kuan Yeh, Been Kim, Sercan Arik, Chun-Liang Li, Tomas Pfister, and Pradeep Ravikumar · 2020
Cited alongside, same era.
Contrastive learning of medical visual representations from paired images and text
Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher D Manning, and Curtis P Langlotz · 2020
Cited alongside, same era.
Modifying memories in transformer models
Chen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix Yu, and Sanjiv Kumar · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Anita Mahinpei, Justin Clark, Isaac Lage, Finale Doshi-Velez, and Weiwei Pan · 2021
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Do concept bottleneck models learn as intended?
Andrei Margeloiu, Matthew Ashman, Umang Bhatt, Yanzhi Chen, Mateja Jamnik, and Adrian Weller · 2021
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Acquisition of chess knowledge in alphazero
Thomas McGrath, Andrei Kapishnikov, Nenad Tomašev, Adam Pearce, Demis Hassabis, Been Kim, Ulrich Paquet, and Vladimir Kramnik · 2021
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Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D Manning · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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A patient-centric dataset of images and metadata for identifying melanomas using clinical context
Veronica Rotemberg, Nicholas Kurtansky, Brigid Betz-Stablein, Liam Caffery, Emmanouil Chousakos, Noel Codella, Marc Combalia, Stephen Dusza, Pascale Guitera, David Gutman, et al · 2021
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Editing a classifier by rewriting its prediction rules
Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, and Aleksander Madry · 2021
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Provable repair of deep neural networks
Matthew Sotoudeh and Aditya V Thakur · 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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Partially interpretable estimators (pie): black-box-refined interpretable machine learning
Tong Wang, Jingyi Yang, Yunyi Li, and Boxiang Wang · 2021
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Meaningfully debugging model mistakes using conceptual counterfactual explanations
Abubakar Abid, Mert Yuksekgonul, and James Zou · 2022
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Concept-level debugging of part-prototype networks
Andrea Bontempelli, Stefano Teso, Fausto Giunchiglia, and Andrea Passerini · 2022
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Weixin Liang and James Zou · 2022
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