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Image classifiers are typically scored on their test set accuracy, but high accuracy can mask a subtle type of model failure.
SVM binary classifier ensembles for image classification
King-Shy Goh, Edward Chang, and Kwang-Ting Cheng · 2001
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80 million tiny images: A large data set for nonparametric object and scene recognition
Antonio Torralba, Rob Fergus, and William T Freeman · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
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Lessons learned from manually classifying CIFAR-10
Andrej Karpathy · 2011
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton · 2013
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 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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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller · 2016
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Deep exploration via bootstrapped dqn
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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A deeper look at dataset bias
Tatiana Tommasi, Novi Patricia, Barbara Caputo, and Tinne Tuytelaars · 2017
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Texture and art with deep neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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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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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Explanations based on the missing: towards contrastive explanations with pertinent negatives
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das · 2018
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Amir Rosenfeld, Richard Zemel, and John K Tsotsos · 2018
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Generalisation in humans and deep neural networks
Robert Geirhos, Carlos R Medina Temme, Jonas Rauber, Heiko H Schütt, Matthias Bethge, and Felix A Wichmann · 2018
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Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
Wieland Brendel and Matthias Bethge · 2019
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A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2019
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Towards automatic concept-based explanations
Amirata Ghorbani, James Wexler, James Y Zou, and Been Kim · 2019
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Probing neural network comprehension of natural language arguments
Timothy Niven and Hung-Yu Kao · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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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
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Pathologies of neural models make interpretations difficult
Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, and Jordan Boyd-Graber · 2018
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Do CIFAR-10 classifiers generalize to CIFAR-10?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2018
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The relative performance of ensemble methods with deep convolutional neural networks for image classification
Cheng Ju, Aurélien Bibaut, and Mark van der Laan · 2018
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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What made you do this? Understanding black-box decisions with sufficient input subsets
Brandon Carter, Jonas Mueller, Siddhartha Jain, and David Gifford · 2019
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Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
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Image synthesis with a single (robust) classifier
Shibani Santurkar, Andrew Ilyas, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
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GradMask: Reduce overfitting by regularizing saliency
Becks Simpson, Francis Dutil, Yoshua Bengio, and Joseph Paul Cohen · 2019
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The (un) reliability of saliency methods
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T Schütt, Sven Dähne, Dumitru Erhan, and Been Kim · 2019
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Why Doctors Aren’t Afraid of Better, More Efficient AI Diagnosing Cancer , 2017
Neel V. Patel · 2020
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Explaining image classifiers by removing input features using generative models
Chirag Agarwal and Anh Nguyen · 2020
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Don’t judge an object by its context: Learning to overcome contextual bias
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
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Saliency is a possible red herring when diagnosing poor generalization
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
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