Many imaging tasks require global information about all pixels in an image.
Conventional bottom-up classification networks globalize information by
decreasing resolution; features are pooled and downsampled into a single
output. But for semantic segmentation and object detection tasks, a network
must provide higher-resolution pixel-level outputs. To globalize information
while preserving resolution, many researchers propose the inclusion of
sophisticated auxiliary blocks, but these come at the cost of a considerable
increase in network size and computational cost. This paper proposes stacked
u-nets (SUNets), which iteratively combine features from different resolution
scales while maintaining resolution. SUNets leverage the information
globalization power of u-nets in a deeper network architectures that is capable
of handling the complexity of natural images. SUNets perform extremely well on
semantic segmentation tasks using a small number of parameters.

本文提出了堆叠 U-Net（SUNet）通过反复迭代不同分辨率尺度的特征来维护分辨率，并利用 U-Net 的信息全球化能力在更深的网络结构中，能够处理自然图像的复杂性，在少量参数的情况下在语义分割任务上表现极佳。

堆叠 U-Net：自然图像分割的无花样方法

Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation

We propose a stochastic extension of the primal-dual hybrid gradient
algorithm studied by Chambolle and Pock in 2011 to solve saddle point problems
that are separable in the dual variable. The analysis is carried out for
general convex-concave saddle point problems and problems that are either
partially smooth / strongly convex or fully smooth / strongly convex. We
perform the analysis for arbitrary samplings of dual variables, and obtain
known deterministic results as a special case. Several variants of our
stochastic method significantly outperform the deterministic variant on a
variety of imaging tasks.

提出一种随机扩展的原始 - 对偶混合梯度算法用于解决在对偶变量中可分离的鞍点问题，该算法适用于一般的凸凹鞍点问题和部分平滑 / 强凸或完全平滑 / 强凸问题，并且在任意抽样的对偶变量中，有多个变体的随机方法在各种图像任务中显着优于确定性变体。