Semi-Supervised Learning with Ladder Networks

Antti Rasmus; Harri Valpola; Mikko Honkala; Mathias Berglund; Tapani Raiko

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arXiv · 1507.02672

Semi-Supervised Learning with Ladder Networks

Antti Rasmus·Harri Valpola·Mikko Honkala·Mathias Berglund·Tapani Raiko

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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.

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BibTeX RIS

Antti Rasmus, Harri Valpola, Mikko Honkala, Mathias Berglund, Tapani Raiko. 2015-07-09. Semi-Supervised Learning with Ladder Networks. https://arxiv.org/abs/1507.02672

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