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Concept-based explanations have emerged as a popular way of extracting human-interpretable representations from deep discriminative models.
The elements of statistical learning: data mining, inference, and prediction
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
Earlier work this paper cites.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
Earlier work this paper cites.
dsprites: Disentanglement testing sprites dataset
Loic Matthey, Irina Higgins, Demis Hassabis, and Alexander Lerchner · 2017
Earlier work this paper cites.
On completeness-aware concept-based explanations in deep neural networks
Chih-Kuan Yeh, Been Kim, Sercan O Arik, Chun-Liang Li, Tomas Pfister, and Pradeep Ravikumar · 2017
Earlier work this paper cites.
Peeking inside the black-box: A survey on explainable artificial intelligence (xai)
Amina Adadi and Mohammed Berrada · 2018
Cited alongside, same era.
Discovering interpretable representations for both deep generative and discriminative models
Tameem Adel, Zoubin Ghahramani, and Adrian Weller · 2018
Cited alongside, same era.
3d shapes dataset
Chris Burgess and Hyunjik Kim · 2018
Cited alongside, same era.
A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 2018
Cited alongside, same era.
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie J. Cai, James Wexler, Fernanda B. Viégas, and Rory Sayres · 2018
Cited alongside, same era.
Machine learning interpretability: A survey on methods and metrics
Flexibly fair representation learning by disentanglement
Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, and Richard Zemel · 2019
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Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2019
Later among the works it cites.
Concept whitening for interpretable image recognition, 2020
Zhi Chen, Yijie Bei, and Cynthia Rudin · 2020
Later among the works it cites.
Now you see me (CME): concept-based model extraction
Dmitry Kazhdan, Botty Dimanov, Mateja Jamnik, Pietro Liò, and Adrian Weller · 2020
Later among the works it cites.
Concept bottleneck models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang · 2020
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Weakly-supervised disentanglement without compromises
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Diogo V Carvalho, Eduardo M Pereira, and Jaime S Cardoso · 2019
Cited alongside, same era.
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, and Michael Tschannen · 2020
Later among the works it cites.