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Image classification models can depend on multiple different semantic attributes of the image.
Causality
Judea Pearl · 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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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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David Hughes, Marcel Salathé, et al · 2015
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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Kedar Dhamdhere, Mukund Sundararajan, and Qiqi Yan · 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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Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy
Jonathan Krause, Varun Gulshan, Ehsan Rahimy, Peter Karth, Kasumi Widner, Greg S Corrado, Lily Peng, and Dale R Webster · 2018
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Diabetic retinopathy detection through integration of deep learning classification framework
Alexander Rakhlin · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Towards automatic concept-based explanations
Amirata Ghorbani, James Wexler, James Zou, and Been Kim · 2019
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Ganalyze: Toward visual definitions of cognitive image properties
Lore Goetschalckx, Alex Andonian, Aude Oliva, and Phillip Isola · 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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Counterfactual visual explanations
Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
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Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind K Mothilal, Amit Sharma, and Chenhao Tan · 2020
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Scientific discovery by generating counterfactuals using image translation
Arunachalam Narayanaswamy, Subhashini Venugopalan, Dale R Webster, Lily Peng, Greg S Corrado, Paisan Ruamviboonsuk, Pinal Bavishi, Michael Brenner, Philip C Nelson, and Avinash V Varadarajan · 2020
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Generative causal explanations of black-box classifiers
Matthew O’Shaughnessy, Gregory Canal, Marissa Connor, Mark Davenport, and Christopher Rozell · 2020
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There and back again: Revisiting backpropagation saliency methods
Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, and Andrea Vedaldi · 2020
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Interpreting the latent space of GANs for semantic face editing
Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou · 2020
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Simple black-box adversarial attacks
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Explanation by progressive exaggeration
Sumedha Singla, Brian Pollack, Junxiang Chen, and Kayhan Batmanghelich · 2019
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Getting a clue: A method for explaining uncertainty estimates
Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, and José Miguel Hernández-Lobato · 2020
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Stargan v2: Diverse image synthesis for multiple domains
Yunjey Choi, Youngjung Uh, Jaejun Yoo, and Jung-Woo Ha · 2020
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Editing in style: Uncovering the local semantics of gans
Edo Collins, Raja Bala, Bob Price, and Sabine Susstrunk · 2020
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A disentangling invertible interpretation network for explaining latent representations
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2020
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Closed-form factorization of latent semantics in GANs
Yujun Shen and Bolei Zhou · 2020
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Semantic pyramid for image generation
Assaf Shocher, Yossi Gandelsman, Inbar Mosseri, Michal Yarom, Michal Irani, William T Freeman, and Tali Dekel · 2020
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Stylespace analysis: Disentangled controls for StyleGAN image generation
Zongze Wu, Dani Lischinski, and Eli Shechtman · 2020
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Attribution in scale and space
Shawn Xu, Subhashini Venugopalan, and Mukund Sundararajan · 2020
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Generative hierarchical features from synthesizing images
Yinghao Xu, Yujun Shen, Jiapeng Zhu, Ceyuan Yang, and Bolei Zhou · 2020
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On completeness-aware concept-based explanations in deep neural networks
Chih-Kuan Yeh, Been Kim, Sercan Arik, Chun-Liang Li, Pradeep Ravikumar, and Tomas Pfister · 2020
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Explaining the black-box smoothly-a counterfactual approach
Sumedha Singla, Brian Pollack, Stephen Wallace, and Kayhan Batmanghelich · 2021
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