Semi-Supervised Learning with Ladder Networks

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Authors Mikko Honkala, Harri Valpola, Tapani Raiko, Antti Rasmus, Mathias Berglund
Journal/Conference Name NeurIPS 2015 12
Paper Category
Paper Abstract We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by backpropagation, avoiding the need for layer-wise pre-training. Our work builds on the Ladder network proposed by Valpola (2015), which we extend by combining the model with supervision. We show that the resulting model reaches state-of-the-art performance in semi-supervised MNIST and CIFAR-10 classification, in addition to permutation-invariant MNIST classification with all labels.
Date of publication 2015
Code Programming Language Multiple
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