Title: An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks,
Abstract: We propose a method for semi-supervised training of structured-output neural
networks. Inspired by the framework of Generative Adversarial Networks (GAN),
we train a discriminator network to capture the notion of a quality of network
output. To this end, we leverage the qualitative difference between outputs
obtained on the labelled training data and unannotated data. We then use the
discriminator as a source of error signal for unlabelled data. This effectively
boosts the performance of a network on a held out test set. Initial experiments
in image segmentation demonstrate that the proposed framework enables achieving
the same network performance as in a fully supervised scenario, while using two
times less annotations.