Abstract
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.
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