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arXiv · 1907.11891

Variational f-divergence Minimization

Mingtian Zhang·Thomas Bird·Raza Habib·Tianlin Xu·David Barber
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Abstract

Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific f-divergence between the model and data distribution. In light of recent successes in training Generative Adversarial Networks, alternative non-likelihood training criteria have been proposed. Whilst not necessarily statistically efficient, these alternatives may better match user requirements such as sharp image generation. A general variational method for training probabilistic latent variable models using maximum likelihood is well established; however, how to train latent variable models using other f-divergences is comparatively unknown. We discuss a variational approach that, when combined with the recently introduced Spread Divergence, can be applied to train a large class of latent variable models using any f-divergence.

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Mingtian Zhang, Thomas Bird, Raza Habib, Tianlin Xu, David Barber. 2019-07-27. Variational f-divergence Minimization. https://arxiv.org/abs/1907.11891

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