论文标题
小波流：高分辨率归一流流量的快速训练
Wavelet Flow: Fast Training of High Resolution Normalizing Flows
论文作者
论文摘要
标准化流是一类概率生成模型，既可以进行快速密度计算和有效的采样，又可以有效地对图像（例如图像）进行建模。当前方法的缺点是它们的巨大培训成本，有时需要数月的GPU培训时间才能获得最新的结果。本文介绍了小波流，这是一种基于小波的多尺度，归一化的流程结构。小波流具有信号尺度的明确表示，该表示本质上包括较低分辨率信号的模型和有条件的高分辨率信号，即超级分辨率。小波流的一个主要优点是能够构建用于高分辨率数据的生成模型（例如1024 x 1024映像），这些模型与以前的模型不切实际。此外，在标准（低分辨率）基准的每个维度方面，小波流动与以前的标准化流相具有竞争力，同时训练的速度快15倍。
Normalizing flows are a class of probabilistic generative models which allow for both fast density computation and efficient sampling and are effective at modelling complex distributions like images. A drawback among current methods is their significant training cost, sometimes requiring months of GPU training time to achieve state-of-the-art results. This paper introduces Wavelet Flow, a multi-scale, normalizing flow architecture based on wavelets. A Wavelet Flow has an explicit representation of signal scale that inherently includes models of lower resolution signals and conditional generation of higher resolution signals, i.e., super resolution. A major advantage of Wavelet Flow is the ability to construct generative models for high resolution data (e.g., 1024 x 1024 images) that are impractical with previous models. Furthermore, Wavelet Flow is competitive with previous normalizing flows in terms of bits per dimension on standard (low resolution) benchmarks while being up to 15x faster to train.
