We introduce $\textit{semi-unsupervised learning}$, an extreme case of semi-supervised learning with ultra-sparse categorisation where some classes have no labels in the training set. That is, in the training data some classes are sparsely labelled and other classes appear only as unlabelled data. Many real-world datasets are conceivably of this type. We demonstrate that effective learning in this regime is only possible when a model is capable of capturing both semi-supervised and unsupervised learning. We develop two deep generative models for classification in this regime that extend previous deep generative models designed for semi-supervised learning. By changing their probabilistic structure to contain a mixture of Gaussians in their continuous latent space, these new models can learn in both unsupervised and semi-unsupervised paradigms. We demonstrate their performance both for semi-unsupervised and unsupervised learning on various standard datasets. We show that our models can learn in an semi-unsupervised manner on Fashion-MNIST. Here we artificially mask out all labels for half of the classes of data and keep $2\%$ of labels for the remaining classes. Our model is able to learn effectively, obtaining a trained classifier with $(77.2\pm1.3)\%$ test set accuracy. We also can train on Fashion-MNIST unsupervised, obtaining $(75.2\pm1.5)\%$ test set accuracy. Additionally, doing the same for MNIST unsupervised we get $(96.3\pm0.9)\%$ test set accuracy, which is state-of-the art for fully probabilistic deep generative models.
翻译:我们引入了 $\ textit{ semi- 不受监督的学习} $( $), 这是一种半监督学习的极端案例, 一些班级在培训组中没有标签。 也就是说, 在培训数据中, 一些班级标签少, 其他班级只是作为无标签的数据。 许多真实世界的数据集都可以想象到这种类型的模式。 我们证明, 只有在模型能够捕捉半监督的和不受监督的数学学习时, 才能在这种制度下进行有效学习。 我们开发了两种深层次的基因化分类模型, 用于扩展先前为半监督学习而设计的深层精度精度精度精度模型。 也就是说, 我们通过改变它们的概率性能结构, 在连续的隐蔽空间中包含高斯人的混合物, 这些新模型可以在不受监督和半监督的范模式中学习。 我们的模型可以用半监督的方式在Fashinon- $( $) 进行半监督的精度精度精度精度精度的精度模型模型模型模型模型模型模型模型模型。, 也用经过人工智能测试的模值 。