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This paper proposes a latent space energy-based prior model forsemi-supervised learning. The model stands on a generator network that maps alatent vector to the observed example. The energy term of the prior modelcouples the latent vector and a symbolic one-hot vector, so that classificationcan be based on the latent vector inferred from the observed example. In ourlearning method, the symbol-vector coupling, the generator network and theinference network are learned jointly. Our method is applicable tosemi-supervised learning in various data domains such as image, text, andtabular data. Our experiments demonstrate that our method performs well onsemi-supervised learning tasks.

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