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Contrastive approaches to representation learning have recently shown great promise.
Nonlinear principal component analysis using autoassociative neural networks
Mark A Kramer · 1991
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A fast algorithm for the minimum covariance determinant estimator
Peter J Rousseeuw and Katrien Van Driessen · 1999
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Lof: identifying density-based local outliers
Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander · 2000
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2002
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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A fast learning algorithm for deep belief nets
Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh · 2006
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Uci machine learning repository, 2007
Arthur Asuncion and David Newman · 2007
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Angle-based outlier detection in high-dimensional data
Hans-Peter Kriegel, Matthias Schubert, and Arthur Zimek · 2008
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Isolation forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2008
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Learning multiple layers of features from tiny images
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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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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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A near-linear time approximation algorithm for angle-based outlier detection in high-dimensional data
Ninh Pham and Rasmus Pagh · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Rapid distance-based outlier detection via sampling
Mahito Sugiyama and Karsten Borgwardt · 2013
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Learning to detect patterns of crime
Tong Wang, Cynthia Rudin, Daniel Wagner, and Rich Sevieri · 2013
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Generalized product of experts for automatic and principled fusion of gaussian process predictions
Yanshuai Cao and David J Fleet · 2014
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Discriminative unsupervised feature learning with convolutional neural networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2014
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Amortized inference in probabilistic reasoning
Samuel Gershman and Noah Goodman · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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The bayesian case model: A generative approach for case-based reasoning and prototype classification
Been Kim, Cynthia Rudin, and Julie A Shah · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Lesinn: Detecting anomalies by identifying least similar nearest neighbours
Guansong Pang, Kai Ming Ting, and David Albrecht · 2015
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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, et al · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2016
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Harrison Edwards and Amos Storkey · 2016
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
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Tadam: Task dependent adaptive metric for improved few-shot learning
Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste · 2018
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Learning representations of ultrahigh-dimensional data for random distance-based outlier detection
Guansong Pang, Longbing Cao, Ling Chen, and Huan Liu · 2018
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Multimodal generative models for scalable weakly-supervised learning
Mike Wu and Noah Goodman · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
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Learning representations by maximizing mutual information across views
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Autonomous driving: technical, legal and social aspects
Markus Maurer, J Christian Gerdes, Barbara Lenz, and Hermann Winner · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Grad-cam: Why did you say that?
Ramprasaath R Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Machine learning and prediction in medicine—beyond the peak of inflated expectations
Jonathan H Chen and Steven M Asch · 2017
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Philip Bachman, R Devon Hjelm, and William Buchwalter · 2019
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Deep learning for anomaly detection: A survey
Raghavendra Chalapathy and Sanjay Chawla · 2019
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Meta-amortized variational inference and learning
Kristy Choi, Mike Wu, Noah Goodman, and Stefano Ermon · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
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Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al · 2019
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Detecting out-of-distribution examples with in-distribution examples and gram matrices
Chandramouli Shama Sastry and Sageev Oore · 2019
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Sofia Serrano and Noah A Smith · 2019
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, et al · 2019
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Attention interpretability across nlp tasks
Shikhar Vashishth, Shyam Upadhyay, Gaurav Singh Tomar, and Manaal Faruqui · 2019
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Pyod: A python toolbox for scalable outlier detection
Yue Zhao, Zain Nasrullah, and Zheng Li · 2019
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Local aggregation for unsupervised learning of visual embeddings
Chengxu Zhuang, Alex Lin Zhai, and Daniel Yamins · 2019
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Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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What makes for good views for contrastive learning
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
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On mutual information in contrastive learning for visual representations
Mike Wu, Chengxu Zhuang, Milan Mosse, Daniel Yamins, and Noah Goodman · 2020
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Deep residual flow for out of distribution detection
Ev Zisselman and Aviv Tamar · 2020
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