论文标题
用嵌套的辍学液归一化的订购尺寸
Ordering Dimensions with Nested Dropout Normalizing Flows
论文作者
论文摘要
标准化流的潜在空间必须与输出空间具有相同的维度。如果我们想学习低维，语义上有意义的表示，则该约束会带来一个问题。最近的工作通过拟合被限制在流形的流量来提供了紧凑的表示，但尚未定义该歧管的密度。在这项工作中，我们考虑在数据空间中提供全部支持的流，但有序的潜在变量。与PCA一样，领先的潜在维度定义了一个靠近数据的歧管序列。我们注意到流量的可能性和排序质量之间的权衡，具体取决于流的参数化。
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.
