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Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks.
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The pascal visual object classes (voc) challenge
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Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance
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Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Discriminative unsupervised feature learning with exemplar convolutional neural networks
A. Dosovitskiy, P. Fischer, J. T. Springenberg, M. Riedmiller, and T. Brox · 2015
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Miguel A Bautista, Artsiom Sanakoyeu, Ekaterina Tikhoncheva, and Björn Ommer · 2016
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Identity mappings in deep residual networks
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Context encoders: Feature learning by inpainting
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Carl Doersch and Andrew Zisserman · 2017
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Adversarial feature learning
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2017
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T Mundhenk, Daniel Ho, and Barry Y. Chen · 2017
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Representation learning by learning to count
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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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Large scale adversarial representation learning, 2019
Jeff Donahue and Karen Simonyan · 2019
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Momentum contrast for unsupervised visual representation learning, 2019
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Deep clustering for unsupervised learning of visual features
M. Caron, P. Bojanowski, A. Joulin, and M. Douze · 2018
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Spyros Gidaris, Praveen Singh, and Nikos Komodakis · 2018
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Simon Jenni and Paolo Favaro · 2018
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Invariant information distillation for unsupervised image segmentation and clustering
Xu Ji, João F Henriques, and Andrea Vedaldi · 2018
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