Neural Architecture Search (NAS) has shown great potentials in automatically
designing scalable network architectures for dense image predictions. However,
existing NAS algorithms usually compromise on restricted search space and
search on proxy task to meet the achievable computational demands. To allow as
wide as possible network architectures and avoid the gap between target and
proxy dataset, we propose a Densely Connected NAS (DCNAS) framework, which
directly searches the optimal network structures for the multi-scale
representations of visual information, over a large-scale target dataset.
Specifically, by connecting cells with each other using learnable weights, we
introduce a densely connected search space to cover an abundance of mainstream
network designs. Moreover, by combining both path-level and channel-level
sampling strategies, we design a fusion module to reduce the memory consumption
of ample search space. We demonstrate that the architecture obtained from our
DCNAS algorithm achieves state-of-the-art performances on public semantic image
segmentation benchmarks, including 84.3% on Cityscapes, and 86.9% on PASCAL VOC
2012. We also retain leading performances when evaluating the architecture on
the more challenging ADE20K and Pascal Context dataset.

本研究提出一种名为 Densely Connected NAS (DCNAS) 的神经架构搜索框架，通过连接细胞并使用可学习的权重来引入密集连接的搜索空间，并通过路径和通道级别的抽样策略设计一个融合模块来降低搜索空间的内存消耗。DCNAS 搜索算法得到的体系结构在公共语义图像分割基准测试中的表现显著优于先前算法。