Most, if not all, modern deep learning systems restrict themselves to a single dataset for neural network training and inference. In this article, we are interested in systematic ways to join datasets that are made of similar purposes. Unlike previous published works that ubiquitously conduct the dataset joining in the uninterpretable latent vectorial space, the core to our method is an augmentation procedure in the label space. The primary challenge to address the label space for dataset joining is the discrepancy between labels: non-overlapping label annotation sets, different labeling granularity or hierarchy and etc. Notably we propose a new technique leveraging artificially created knowledge graph, recurrent neural networks and policy gradient that successfully achieve the dataset joining in the label space. Empirical results on both image and text classification justify the validity of our approach.

本文旨在探讨在标签空间中通过增强技术进行数据集的加入，并提出了一种通过人工创建的知识图谱、递归神经网络和策略梯度等技术成功实现标签空间下数据集的加入的新技术。经过图像和文本分类的实证结果证明了我们方法的有效性。

通过标记空间中的数据增强方法联合神经网络数据集