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Contrastive, self-supervised learning of object representations recently emerged as an attractive alternative to reconstruction-based training.
Objective criteria for the evaluation of clustering methods
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Klaus Greff, Sjoerd Van Steenkiste, and Jürgen Schmidhuber · 2017
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Charlie Nash, SM Ali Eslami, Chris Burgess, Irina Higgins, Daniel Zoran, Theophane Weber, and Peter Battaglia · 2017
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Sjoerd Van Steenkiste, Michael Chang, Klaus Greff, and Jürgen Schmidhuber · 2018
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Karl Stelzner, Robert Peharz, and Kristian Kersting · 2019
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Spatially invariant unsupervised object detection with convolutional neural networks
Eric Crawford and Joelle Pineau · 2019
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Scalable object-oriented sequential generative models
Jindong Jiang, Sepehr Janghorbani, Gerard de Melo, and Sungjin Ahn · 2019
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Spatial broadcast decoder: A simple architecture for learning disentangled representations in vaes
Nicholas Watters, Loic Matthey, Christopher P Burgess, and Alexander Lerchner · 2019
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Rishabh Kabra, Chris Burgess, Loic Matthey, Raphael Lopez Kaufman, Klaus Greff, Malcolm Reynolds, and Alexander Lerchner · 2019
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R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2018
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Unsupervised learning of object keypoints for perception and control
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Momentum contrast for unsupervised visual representation learning
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