Recently, Neural Architecture Search (NAS) has successfully identified neural
network architectures that exceed human designed ones on large-scale image
classification. In this paper, we study NAS for semantic image segmentation.
Existing works often focus on searching the repeatable cell structure, while
hand-designing the outer network structure that controls the spatial resolution
changes. This choice simplifies the search space, but becomes increasingly
problematic for dense image prediction which exhibits a lot more network level
architectural variations. Therefore, we propose to search the network level
structure in addition to the cell level structure, which forms a hierarchical
architecture search space. We present a network level search space that
includes many popular designs, and develop a formulation that allows efficient
gradient-based architecture search (3 P100 GPU days on Cityscapes images). We
demonstrate the effectiveness of the proposed method on the challenging
Cityscapes, PASCAL VOC 2012, and ADE20K datasets. Auto-DeepLab, our
architecture searched specifically for semantic image segmentation, attains
state-of-the-art performance without any ImageNet pretraining.

本文通过搜索单元结构和网络结构两个不同的层次来提高图像分割性能，并设计出了一种有效的基于梯度的架构搜索方法，其结果表明，在不进行 ImageNet 预训练的情况下，Auto-DeepLab 可以达到最先进的性能水平。