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Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering.
A new measure of rank correlation
Maurice G Kendall · 1938
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The mnist database of handwritten digits
Yann LeCun · 1998
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Sampling matters in deep embedding learning
Chao-Yuan Wu, R Manmatha, Alexander J Smola, and Philipp Krähenbühl · 1998
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The information bottleneck method
N. Tishby, F.C. Pereira, and W. Biale · 1999
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Dimensionality reduction by learning an invariant mapping
R. Hadsell, S. Chopra, and Y. LeCun · 2006
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Neural codes for image retrieval
Artem Babenko, Anton Slesarev, Alexandr Chigorin, and Victor Lempitsky · 2014
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Efficient non-parametric estimation of multiple embeddings per word in vector space
Arvind Neelakantan, Jeevan Shankar, Alexandre Passos, and Andrew McCallum · 2014
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Word representations via gaussian embedding
Luke Vilnis and Andrew McCallum · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Bayesian representation learning with oracle constraints
Theofanis Karaletsos, Serge Belongie, and Gunnar Rätsch · 2015
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Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Deep variational information bottleneck
Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy · 2016
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Identifying unknown unknowns in the open world: Representations and policies for guided exploration, 2017
H Lakkaraju, E Kamar, R Caruana, and E Horvitz · 2017
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No fuss distance metric learning using proxies
Yair Movshovitz-Attias, Alexander Toshev, Thomas K Leung, Sergey Ioffe, and Saurabh Singh · 2017
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Deep metric learning via facility location
Hyun Oh Song, Stefanie Jegelka, Vivek Rathod, and Kevin Murphy · 2017
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On the emergence of invariance and disentangling in deep representations
Alessandro Achille and Stefano Soatto · 2018
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Uncertainty in the variational information bottleneck
Alexander A Alemi, Ian Fischer, and Joshua V Dillon · 2018
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Deep gaussian embedding of attributed graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Günnemann · 2018
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Luca Bertinetto, Jack Valmadre, João F Henriques, Andrea Vedaldi, and Philip HS Torr · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Toward Open-Set face recognition
Manuel Günther, Steve Cruz, Ethan M Rudd, and Terrance E Boult · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Understanding and controlling user linkability in decentralized learning
Tribhuvanesh Orekondy, Seong Joon Oh, Bernt Schiele, and Mario Fritz · 2018
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Rethinking feature distribution for loss functions in image classification
Weitao Wan, Yuanyi Zhong, Tianpeng Li, and Jiansheng Chen · 2018
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