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We build new test sets for the CIFAR-10 and ImageNet datasets.
Wordnet: A lexical database for english
George A. Miller · 1995
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli · 2004
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Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2005
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Dataset issues in object recognition
Jean Ponce, Tamara L. Berg, Mark Everingham, David A. Forsyth, Martial Hebert, Sveltana Lazebnik, Marcin Marszalek, Cordelia Schmid, Bryan C. Russell, Antionio Torralba, Chris. K. I. Williams, Jianguo Zhang, and Andrew Zisserman · 2006
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XRCE’s participation to ImagEval
Stephane Clinchant, Gabriela Csurka, Florent Perronnin, and Jean-Michel Renders · 2007
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Introduction to a large-scale general purpose ground truth database: methodology, annotation tool and benchmarks
Benjamin Z. Yao, Xiong Yang, and Song-Chun Zhu · 2007
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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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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning
Ali Rahimi and Benjamin Recht · 2009
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The Pascal Visual Object Classes (VOC) challenge
Mark Everingham, Luc Gool, Christopher K. Williams, John Winn, and Andrew Zisserman · 2010
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Improving the Fisher kernel for large-scale image classification
Florent Perronnin, Jorge Sánchez, and Thomas Mensink · 2010
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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Lessons learned from manually classifying CIFAR-10
Andrej Karpathy · 2011
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Unbiased look at dataset bias
Antonio Torralba and Alexei A. Efros · 2011
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Large Scale Visual Recognition
Jia Deng · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2013
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Microsoft COCO: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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The Ladder: A reliable leaderboard for machine learning competitions
Avrim Blum and Moritz Hardt · 2015
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Manitest: Are classifiers really invariant?
Alhussein Fawzi and Pascal Frossard · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Very deep convolutional neural network based image classification using small training sample size
Shuying Liu and Weihong Deng · 2015
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ImageNet: Where have we been? where are we going?
Fei-Fei Li and Jia Deng · 2017
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Technical perspective: What led computer vision to deep learning?
Jitendra Malik · 2017
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Inception-v4, Inception-Resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A. Alemi · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Polynet: A pursuit of structural diversity in very deep networks
Xingcheng Zhang, Zhizhong Li, Chen Change Loy, and Dahua Lin · 2017
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Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Fei-Fei Li · 2015
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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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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, and Kurt Keutzer · 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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Sergey Zagoruyko and Nikos Komodakis · 2016
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Yunpeng Chen, Jianan Li, Huaxin Xiao, Xiaojie Jin, Shuicheng Yan, and Jiashi Feng · 2017
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
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Improved regularization of convolutional neural networks with Cutout
Terrance DeVries and Graham W Taylor · 2017
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Personal communication, 2018
Alex Berg · 2018
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Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
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AutoAugment: Learning augmentation policies from data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le · 2018
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Generalisation in humans and deep neural networks
Robert Geirhos, Carlos R. M. Temme, Jonas Rauber, Heiko H. Schütt, Matthias Bethge, and Felix A. Wichmann · 2018
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Hossein Hosseini and Radha Poovendran · 2018
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Geometric robustness of deep networks: Analysis and improvement
Can Kanbak, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2018
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Do better ImageNet models transfer better?
Simon Kornblith, Jonathon Shlens, and Quoc V. Le · 2018
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Progressive neural architecture search
Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy · 2018
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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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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le · 2018
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Spatially transformed adversarial examples
Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, and Dawn Song · 2018
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Yoshihiro Yamada, Masakazu Iwamura, and Koichi Kise · 2018
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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