1 research outputs found
Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows
As data volumes continue to grow, the labelling process increasingly becomes
a bottleneck, creating demand for methods that leverage information from
unlabelled data. Impressive results have been achieved in semi-supervised
learning (SSL) for image classification, nearing fully supervised performance,
with only a fraction of the data labelled. In this work, we propose a
probabilistically principled general approach to SSL that considers the
distribution over label predictions, for labels of different complexity, from
"one-hot" vectors to binary vectors and images. Our method regularises an
underlying supervised model, using a normalising flow that learns the posterior
distribution over predictions for labelled data, to serve as a prior over the
predictions on unlabelled data. We demonstrate the general applicability of
this approach on a range of computer vision tasks with varying output
complexity: classification, attribute prediction and image-to-image
translation