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With the maturing of deep learning systems, trustworthiness is becoming increasingly important for model assessment.
The information bottleneck method
Naftali Tishby, Fernando C. N. Pereira, and William Bialek · 2000
Earlier work this paper cites.
On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes
Andrew Y. Ng and Michael I. Jordan · 2001
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The tradeoff between generative and discriminative classifiers
Guillaume Bouchard and Bill Triggs · 2004
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Classification with hybrid generative/discriminative models
Rajat Raina, Yirong Shen, Andrew Mccallum, and Andrew Y Ng · 2004
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Comparison of generative and discriminative techniques for object detection and classification
Ilkay Ulusoy and Christopher M. Bishop · 2006
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Generative or discriminative? getting the best of both worlds
Christopher M. Bishop and Julia Lasserre · 2007
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Generative and discriminative algorithms for spoken language understanding
Christian Raymond and Giuseppe Riccardi · 2007
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On the generative-discriminative tradeoff approach: Interpretation, asymptotic efficiency and classification performance
Jing-Hao Xue and D. M. Titterington · 2010
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A family of nonparametric density estimation algorithms
Esteban G Tabak and Cristina V Turner · 2013
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NICE: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Semi-supervised learning with deep generative models
Durk P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
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ImageNet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2015
Earlier work this paper cites.
Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Improving variational auto-encoders using householder flow
Jakub M Tomczak and Max Welling · 2016
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner · 2017
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Triple generative adversarial nets
LI Chongxuan, Taufik Xu, Jun Zhu, and Bo Zhang · 2017
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A learned representation for artistic style
Vincent Dumoulin, Jonathon Shlens, and Manjunath Kudlur · 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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Jens Behrmann, David Duvenaud, and Jörn-Henrik Jacobsen · 2018
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Waic, but why? generative ensembles for robust anomaly detection
Semi-supervised learning with normalizing flows
Pavel Izmailov, Polina Kirichenko, Marc Finzi, and Andrew Gordon Wilson · 2019
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Robust inference via generative classifiers for handling noisy labels
Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, and Jinwoo Shin · 2019
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Are generative classifiers more robust to adversarial attacks?
Yingzhen Li, John Bradshaw, and Yash Sharma · 2019
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Do deep generative models know what they don’t know?
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, and Balaji Lakshminarayanan · 2019
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Hybrid models with deep and invertible features
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, and Balaji Lakshminarayanan · 2019
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Hyunsun Choi, Eric Jang, and Alexander A Alemi · 2018
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Explaining explanations: An overview of interpretability of machine learning
Leilani H Gilpin, David Bau, Ben Z Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal · 2018
Cited alongside, same era.
Ffjord: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky TQ Chen, Jesse Betterncourt, Ilya Sutskever, and David Duvenaud · 2018
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A survey of safety and trustworthiness of deep neural networks
Xiaowei Huang, Daniel Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu, and Xinping Yi · 2018
Cited alongside, same era.
Excessive invariance causes adversarial vulnerability
Jörn-Henrik Jacobsen, Jens Behrmann, Richard Zemel, and Matthias Bethge · 2018
Cited alongside, same era.
i-RevNet: deep invertible networks
Jörn-Henrik Jacobsen, Arnold W.M. Smeulders, and Edouard Oyallon · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Diederik P Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, and Balaji Lakshminarayanan · 2019
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Towards the first adversarially robust neural network model on MNIST
Lukas Schott, Jonas Rauber, Matthias Bethge, and Wieland Brendel · 2019
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Input complexity and out-of-distribution detection with likelihood-based generative models
Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F Núñez, and Jordi Luque · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D Sculley, Joshua Dillon, Jie Ren, and Zachary Nado · 2019
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Unsupervised out-of-distribution detection with batch normalization
Jiaming Song, Yang Song, and Stefano Ermon · 2019
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Learning likelihoods with conditional normalizing flows
Christina Winkler, Daniel Worrall, Emiel Hoogeboom, and Max Welling · 2019
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Adversarial attacks and defenses in images, graphs and text: A review
Han Xu, Yao Ma, Haochen Liu, Debayan Deb, Hui Liu, Jiliang Tang, and Anil Jain · 2019
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Training normalizing flows with the information bottleneck for competitive generative classification
Lynton Ardizzone, Radek Mackowiak, Carsten Rother, and Ullrich Köthe · 2020
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Robust out-of-distribution detection in neural networks
Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, and Somesh Jha · 2020
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Understanding the limitations of conditional generative models
Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl, and Richard S. Zemel · 2020
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Generalized ODIN: detecting out-of-distribution image without learning from out-of-distribution data
Yen-Chang Hsu, Yilin Shen, Hongxia Jin, and Zsolt Kira · 2020
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Why normalizing flows fail to detect out-of-distribution data
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2020
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Out-of-distribution detection with distance guarantee in deep generative models
Yufeng Zhang, Wanwei Liu, Zhenbang Chen, Ji Wang, Zhiming Liu, Kenli Li, Hongmei Wei, and Zuoning Chen · 2020
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