Flow Contrastive Estimation of Energy-Based Models
CVPR, pp. 7515-7525, 2019.
Efficient evaluation of the log-density allows flow models to be directly optimized towards the log-likelihood objective, unlike variational autoencoders, which are optimized towards a bound on the log-likelihood, and generative adversarial networks
This paper studies a training method to jointly estimate an energy-based model and a flow-based model, in which the two models are iteratively updated based on a shared adversarial value function. This joint training method has the following traits. (1) The update of the energy-based model is based on noise contrastive estimation, with ...More
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