论文标题
增强标准化流量：桥接生成流和潜在变量模型之间的差距
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models
论文作者
论文摘要
在这项工作中，我们在增强数据空间上提出了一个新的生成流量家庭，目的是提高表现力，而不会大幅度增加对可能性下限制的采样和评估的计算成本。从理论上讲，我们证明所提出的流量可以将哈密顿颂视为通用传输图。从经验上讲，我们在基于流量的生成建模的标准基准上展示了最先进的性能。
In this work, we propose a new family of generative flows on an augmented data space, with an aim to improve expressivity without drastically increasing the computational cost of sampling and evaluation of a lower bound on the likelihood. Theoretically, we prove the proposed flow can approximate a Hamiltonian ODE as a universal transport map. Empirically, we demonstrate state-of-the-art performance on standard benchmarks of flow-based generative modeling.
