Normalizing flow is a generative modeling approach with efficient sampling.
However, Flow-based models suffer two issues, which are manifold and discrete
data. If the target distribution is a manifold, which means the dimension of
the latent target distribution and the dimension of the data distribution are
unmatched, flow-based models might perform badly. Discrete data makes
flow-based models collapse into a degenerate mixture of point masses. In this
paper, to sidestep such two issues we propose PaddingFlow, a novel
dequantization method, which improves normalizing flows with
padding-dimensional noise. PaddingFlow is easy to implement, computationally
cheap, widely suitable for various tasks, and generates samples that are
unbiased estimations of the data. Especially, our method can overcome the
limitation of existing dequantization methods that have to change the data
distribution, which might degrade performance. We validate our method on the
main benchmarks of unconditional density estimation, including five tabular
datasets and four image datasets for VAE models, and the IK experiments which
are conditional density estimation. The results show that PaddingFlow can
provide improvement on all tasks in this paper.

PaddingFlow 是一种新颖的去量化方法，通过在流模型中引入填充维度噪声，克服了正则化流模型在具有流形或离散数据时的性能问题，并在无条件密度估计的主要基准测试和条件密度估计的 IK 实验中展示出改进性能。

PaddingFlow：通过填充维度噪声改进标准化流

PaddingFlow: Improving Normalizing Flows with Padding-Dimensional Noise

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。