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Deep neural networks have achieved great success in classification tasks during the last years.
Foundations of Statistical Natural Language Processing
Christopher D. Manning and Hinrich Schütze · 1999
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The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2001
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On choosing and bounding probability metrics
Alison L. Gibbs and Francis Edward Su · 2002
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Convex Optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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Robust out-of-distribution detection via informative outlier mining
Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, and Somesh Jha · 2006
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The relationship between precision-recall and roc curves
Jesse Davis and Mark Goadrich · 2006
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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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Encyclopedia of Distances
Michel Marie Deza and Elena Deza · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts · 2013
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Towards open world recognition
Abhijit Bendale and Terrance Boult · 2015
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Cited alongside, same era.
Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory F. Cooper, and Milos Hauskrecht · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Mai Nguyen, Jason Yosinski, and Jeff Clune · 2015
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 2015
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Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations
Dan Hendrycks and Thomas G. Dietterich · 2018
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Novelty Detection with GAN
Mark Kliger and Shachar Fleishman · 2018
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Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Shiyu Liang, Yixuan Li, and R. Srikant · 2018
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Open Category Detection with PAC Guarantees
Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, and Dan Hendrycks · 2018
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Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark Gales · 2018
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Places: A 10 million image database for scene recognition
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba · 2018
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Learning local feature descriptors with triplets and shallow convolutional neural networks
Vassileios Balntas, Edgar Riba, Daniel Ponsa, and Krystian Mikolajczyk · 2016
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Multi30K: Multilingual English-German Image Descriptions
Desmond Elliott, Stella Frank, Khalil Sima’an, and Lucia Specia · 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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Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions
Vijay Kumar, Gustavo Carneiro, and Ian Reid · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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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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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
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Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem
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Deep anomaly detection with outlier exposure
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Likelihood Ratios for Out-of-Distribution Detection
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, and Balaji Lakshminarayanan · 2019
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Unsupervised out-of-distribution detection using kernel density estimation
Ertunc Erdil, Krishna Chaitanya, and Ender Konukoglu · 2020
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Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data
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Towards neural networks that provably know when they don’t know
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Self-supervised learning for generalizable out-of-distribution detection
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Density of states estimation for out-of-distribution detection
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Detecting out-of-distribution examples with gram matrices
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Contrastive training for improved out-of-distribution detection
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Deep residual flow for out of distribution detection
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