# Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows

Source: [Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows](<https://www.sota2.com/research/paper/68fa67eb1e209131cea191fd>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:25:00.051Z

Authors: Ivana Bala\\v{z}evi\\'c, Carl Allen, Timothy Hospedales

Published: 2020-07-06T13:36:00.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2007.02745>)

[PDF](<https://arxiv.org/pdf/2007.02745.pdf>)