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Open Access Publications from the University of California

Conditional Divergence Triangle for Joint Training of Generator, Energy-based and Inference Models

  • Author(s): Zhu, Shuai
  • Advisor(s): Wu, Yingnian
  • et al.

This paper proposes a conditional version of Divergence Triangle [1] as a framework to train generator, energy-based and inference models jointly with the information of labels, where the learning of the above three models are integrated perfectly in a unified probabilistic formulation. Experiments demonstrate that, within this one framework, we are able to complete the following tasks together, (1) control the fine-grained categories to generate realistic images, (2) obtain the meaningful representation of observed data in the low dimensions, and also (3) conduct label classification on unobserved data. Additionally, I also discuss a possible extension on Conditional Divergence Triangle model at the end of this paper for future work.

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