论文标题
学习基于图形的先验，用于广义零射门学习
Learning Graph-Based Priors for Generalized Zero-Shot Learning
论文作者
论文摘要
零射门学习（ZSL）的任务需要正确预测训练时看不见的类样品标签。这是通过利用有关类标签的侧面信息来实现的，例如标签属性或单词嵌入。最近，注意力转移到了广义ZSL（GZSL）的更现实的任务中，其中测试集由可见和看不见的样本组成。 GZSL的最新方法显示了生成模型的价值，这些模型用于从看不见的类别生成样品。在这项工作中，我们以标签上的关系图的形式结合了侧面信息的其他来源。我们利用此图来学习一组先前的分布，这些分布鼓励一个对齐的变异自动编码器（VAE）模型学习尊重图形结构的嵌入。使用这种方法，我们能够在强大的基线上实现在CUB和Sun -Benchmarks上的提高性能。
The task of zero-shot learning (ZSL) requires correctly predicting the label of samples from classes which were unseen at training time. This is achieved by leveraging side information about class labels, such as label attributes or word embeddings. Recently, attention has shifted to the more realistic task of generalized ZSL (GZSL) where test sets consist of seen and unseen samples. Recent approaches to GZSL have shown the value of generative models, which are used to generate samples from unseen classes. In this work, we incorporate an additional source of side information in the form of a relation graph over labels. We leverage this graph in order to learn a set of prior distributions, which encourage an aligned variational autoencoder (VAE) model to learn embeddings which respect the graph structure. Using this approach we are able to achieve improved performance on the CUB and SUN benchmarks over a strong baseline.
