Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling

Bo Pang, Erik Nijkamp, Jiali Cui, Tian Han, Ying Nian Wu

Keywords: generative model, semi-supervised learning, energy-based model

TL;DR: We propose a latent space energy-based prior model for semi-supervised learning which is generally applicable to image, text, and tabular data.

Abstract: 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.