Ordering Dimensions with Nested Dropout Normalizing Flows
University of Edinburgh（爱丁堡大学）
英文摘要：
The latent space of normalizing flows must be of the same dimensionality as their output space. This constraint presents a problem if we want to learn low-dimensional, semantically meaningful representations. Recent work has provided compact representations by fitting flows constrained to manifolds, but hasn't defined a density off that manifold. In this work we consider flows with full support in data space, but with ordered latent variables. Like in PCA, the leading latent dimensions define a sequence of manifolds that lie close to the data. We note a trade-off between the flow likelihood and the quality of the ordering, depending on the parameterization of the flow.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
