We present a deep generative model for learning to predict classes not seen at training time. Unlike most existing methods for this problem, that represent each class as a point (via a semantic embedding), we represent each seen/unseen class using a class-specific latent-space distribution, conditioned on class attributes. We use these latent-space distributions as a prior for a supervised variational autoencoder (VAE), which also facilitates learning highly discriminative feature representations for the inputs. The entire framework is learned end-to-end using only the seen-class training data. The model infers corresponding attributes of a test image by maximizing the VAE lower bound; the inferred attributes may be linked to labels not seen when training. We further extend our model to a (1) semi-supervised/transductive setting by leveraging unlabeled unseen-class data via an unsupervised learning module, and (2) few-shot learning where we also have a small number of labeled inputs from the unseen classes. We compare our model with several state-of-the-art methods through a comprehensive set of experiments on a variety of benchmark data sets.

本论文提出了一个利用深度生成模型进行类预测的研究方法，不同于其他类似方法依赖于语义嵌入，我们基于属性条件设计了特定于类别的潜在空间分布，并利用这些潜在空间分布作为监督变分自编码器的先验，使其有更强的区分特征表达能力；同时通过无监督学习利用未标记的未见类别数据进行半监督 / 转导学习，并在少样本学习下与现有方法进行了对比测试。

基于类条件深度生成模型的零样本学习