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Improved Variational Inference with Inverse Autoregressive Flow

NIPS Proceedingsβ

Improved Variational Inference with Inverse Autoregressive Flow

Part of: Advances in Neural Information Processing Systems 29 (NIPS 2016)

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Authors

Conference Event Type: Poster

Abstract

The framework of normalizing flows provides a general strategy for flexible variational inference of posteriors over latent variables. We propose a new type of normalizing flow, inverse autoregressive flow (IAF), that, in contrast to earlier published flows, scales well to high-dimensional latent spaces. The proposed flow consists of a chain of invertible transformations, where each transformation is based on an autoregressive neural network. In experiments, we show that IAF significantly improves upon diagonal Gaussian approximate posteriors. In addition, we demonstrate that a novel type of variational autoencoder, coupled with IAF, is competitive with neural autoregressive models in terms of attained log-likelihood on natural images, while allowing significantly faster synthesis.