  The design of neural network architectures is an important component for
achieving state-of-the-art performance with machine learning systems across a
broad array of tasks. Much work has endeavored to design and build
architectures automatically through clever construction of a search space
paired with simple learning algorithms. Recent progress has demonstrated that
such meta-learning methods may exceed scalable human-invented architectures on
image classification tasks. An open question is the degree to which such
methods may generalize to new domains. In this work we explore the construction
of meta-learning techniques for dense image prediction focused on the tasks of
scene parsing, person-part segmentation, and semantic image segmentation.
Constructing viable search spaces in this domain is challenging because of the
multi-scale representation of visual information and the necessity to operate
on high resolution imagery. Based on a survey of techniques in dense image
prediction, we construct a recursive search space and demonstrate that even
with efficient random search, we can identify architectures that outperform
human-invented architectures and achieve state-of-the-art performance on three
dense prediction tasks including 82.7\% on Cityscapes (street scene parsing),
71.3\% on PASCAL-Person-Part (person-part segmentation), and 87.9\% on PASCAL
VOC 2012 (semantic image segmentation). Additionally, the resulting
architecture is more computationally efficient, requiring half the parameters
and half the computational cost as previous state of the art systems.
