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MaCow: Masked Convolutional Generative Flow
[article]
2019
arXiv
pre-print
Flow-based generative models, conceptually attractive due to tractability of both the exact log-likelihood computation and latent-variable inference, and efficiency of both training and sampling, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations. Despite their computational efficiency, the density estimation performance of flow-based generative models significantly falls behind those of state-of-the-art autoregressive models.
arXiv:1902.04208v5
fatcat:u4djxn3hwjf4ljwd63j7akyrwm