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Temporal Ensembling for Semi-Supervised Learning
[article]
2017
arXiv
pre-print
In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions. This ensemble prediction can be expected to be a better predictor for the unknown
arXiv:1610.02242v3
fatcat:x4urhkedibd7za6yqlp2v7hj2a