Flow-based generative models are powerful exact likelihood models with
efficient sampling and inference. Despite their computational efficiency,
flow-based models generally have much worse density modeling performance
compared to state-of-the-art autoregressive models. In this paper, we
investigate and improve upon three limiting design choices employed by
flow-based models in prior work: the use of uniform noise for dequantization,
the use of inexpressive affine flows, and the use of purely convolutional
conditioning networks in coupling layers. Based on our findings, we propose
Flow++, a new flow-based model that is now the state-of-the-art
non-autoregressive model for unconditional density estimation on standard image
benchmarks. Our work has begun to close the significant performance gap that
has so far existed between autoregressive models and flow-based models. Our
implementation is available at this https URL

通过改进流媒体生成模型的限定设计选择，包括去量化时使用均匀噪声、使用不可表述的仿射流以及在耦合层中使用纯卷积神经网络，我们提出了一种新的流基模型 Flow ++，它在标准图像基准上是现在最先进的非自回归模型，开始缩小自回归模型和流媒体模型之间显著的性能差距，并提供了代码实现。