  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.
