论文标题
通过潜在空间基于符号矢量耦合的潜在空间能量模型的半监督学习
Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling
论文作者
论文摘要
本文提出了一个基于半监督学习的潜在空间能量的先验模型。该模型位于发电机网络上，该网络将潜在向量映射到观察到的示例。先前模型的能量项将潜在向量和符号的一hot矢量耦合，因此分类可以基于从观察到的示例中推断出的潜在向量。在我们的学习方法中，共同学习符号矢量耦合，发电机网络和推理网络。我们的方法适用于各种数据域中的半监督学习，例如图像，文本和表格数据。我们的实验表明，我们的方法在半监督的学习任务上表现良好。
This paper proposes a latent space energy-based prior model for semi-supervised learning. The model stands on a generator network that maps a latent vector to the observed example. The energy term of the prior model couples the latent vector and a symbolic one-hot vector, so that classification can be based on the latent vector inferred from the observed example. In our learning method, the symbol-vector coupling, the generator network and the inference network are learned jointly. Our method is applicable to semi-supervised learning in various data domains such as image, text, and tabular data. Our experiments demonstrate that our method performs well on semi-supervised learning tasks.
