The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class. The recently introduced meta-learning approaches tackle this problem by learning a generic classifier across a large number of multiclass classification tasks and generalizing the model to a new task. Yet, even with such meta-learning, the low-data problem in the novel classification task still remains. In this paper, we propose Transductive Propagation Network (TPN), a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem. Specifically, we propose to learn to propagate labels from labeled instances to unlabeled test instances, by learning a graph construction module that exploits the manifold structure in the data. TPN jointly learns both the parameters of feature embedding and the graph construction in an end-to-end manner. We validate TPN on multiple benchmark datasets, on which it largely outperforms existing few-shot learning approaches and achieves the state-of-the-art results.
翻译：短片学习的目的是学习一个分类,即使每班培训数量有限的培训案例也能够很好地概括这一分类。最近引入的元学习方法通过在大量多类分类任务中学习通用分类方法来解决这个问题,并将模型推广到新的任务中。然而,即使有了这种元学习,新分类任务中的低数据问题仍然存在。在本文件中,我们提议了Trantaive propagation Network(TPN),这是一个新的转基因推理元学习框架,它将整个测试集同时分类,以减轻低数据问题。具体地说,我们提议学习将标签标签标签从标签实例传播到无标签的测试实例,通过学习利用数据中多重结构的图形构建模块。主题方案网络以端对端方式共同学习特征嵌入参数和图形构造的参数。我们在多个基准数据集上验证了主题方案网络,它基本上超越了现有的微小的学习方法,并实现了最新的结果。