Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model

CVPR 2019 Tian Han Erik Nijkamp Xiaolin Fang Mitch Hill Song-Chun Zhu Ying Nian Wu

This paper proposes the divergence triangle as a framework for joint training of a generator model, energy-based model and inference model. The divergence triangle is a compact and symmetric (anti-symmetric) objective function that seamlessly integrates variational learning, adversarial learning, wake-sleep algorithm, and contrastive divergence in a unified probabilistic formulation... (read more)

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