Semi-Supervised Learning with Ladder Networks
This work addresses the challenge of leveraging unlabeled data for improved classification accuracy in machine learning, though it is incremental as it builds on an existing ladder network.
The paper tackled the problem of semi-supervised learning by combining supervised and unsupervised training in deep neural networks, achieving state-of-the-art performance on MNIST and CIFAR-10 classification tasks.
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.