论文标题
通过标准化流量学习半监督学习的预测分布
Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows
论文作者
论文摘要
随着数据量的不断增长，标记过程越来越多地变成瓶颈，从而产生了对无标记数据信息的方法的需求。半监督学习（SSL）的图像分类已经取得了令人印象深刻的结果，即接近完全监督的性能，只有一小部分被标记的数据。在这项工作中，我们提出了一种对SSL的概率原则性的一般方法，该方法考虑了标签预测上的分布，从“一击”向量到二进制向量和图像的标签。我们的方法使用标准化流量来了解标记数据的预测的后验分布，以作为对未标记数据的预测的先验，从而定期进行基础监督模型。我们证明了这种方法在具有不同输出复杂性的一系列计算机视觉任务上的一般适用性：分类，属性预测和图像到图像转换。
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.
