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Self-supervised contrastive learning offers a means of learning informative features from a pool of unlabeled data.
Nist special database 19
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Gradient-based learning applied to document recognition
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Core vector machines: Fast svm training on very large data sets
Ivor W Tsang, James T Kwok, and Pak-Ming Cheung · 2005
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Smaller coresets for k-median and k-means clustering
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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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Learning mixtures of submodular functions for image collection summarization
Sebastian Tschiatschek, Rishabh K Iyer, Haochen Wei, and Jeff A Bilmes · 2014
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Submodular subset selection for large-scale speech training data
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Submodularity in data subset selection and active learning
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Jonathan Huggins, Trevor Campbell, and Tamara Broderick · 2016
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An empirical study of example forgetting during deep neural network learning
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Unsupervised data selection for supervised learning
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Learning and data selection in big datasets
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Momentum contrast for unsupervised visual representation learning
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A survey of deep learning-based object detection
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Cold case: The lost mnist digits
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A simple framework for contrastive learning of visual representations
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Improved baselines with momentum contrastive learning
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