Abstract

Flow-based generative models, conceptually attractive due to tractability of the exact log-likelihood computation and latent-variable inference as well as efficiency in training and sampling, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations. Despite computational efficiency, the density estimation performance of flow-based generative models significantly falls behind those of state-of-the-art autoregressive models. In this work, we introduce masked convolutional generative flow (M AC OW), a simple yet effective architecture for generative flow using masked convolution. By restricting the local connectivity to a small kernel, M AC OW features fast and stable training along with efficient sampling while achieving significant improvements over Glow for density estimation on standard image benchmarks, considerably narrowing the gap with autoregressive models.