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Conditional Divergence Triangle for Joint Training of Generator, Energy-based and Inference Models
- Zhu, Shuai
- Advisor(s): Wu, Yingnian
Abstract
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.
Main Content
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