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

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

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arXiv · 1812.10907

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

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

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Abstract

This paper proposes the divergence triangle as a framework for joint training of 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. This unification makes the processes of sampling, inference, energy evaluation readily available without the need for costly Markov chain Monte Carlo methods. Our experiments demonstrate that the divergence triangle is capable of learning (1) an energy-based model with well-formed energy landscape, (2) direct sampling in the form of a generator network, and (3) feed-forward inference that faithfully reconstructs observed as well as synthesized data. The divergence triangle is a robust training method that can learn from incomplete data.

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Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu. 2018-12-28. Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model. https://arxiv.org/abs/1812.10907

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