  Automated design of neural network architectures tailored for a specific task
is an extremely promising, albeit inherently difficult, avenue to explore.
While most results in this domain have been achieved on image classification
and language modelling problems, here we concentrate on dense per-pixel tasks,
in particular, semantic image segmentation using fully convolutional networks.
In contrast to the aforementioned areas, the design choices of a fully
convolutional network require several changes, ranging from the sort of
operations that need to be used---e.g., dilated convolutions---to a solving of
a more difficult optimisation problem. In this work, we are particularly
interested in searching for high-performance compact segmentation
architectures, able to run in real-time using limited resources. To achieve
that, we intentionally over-parameterise the architecture during the training
time via a set of auxiliary cells that provide an intermediate supervisory
signal and can be omitted during the evaluation phase. The design of the
auxiliary cell is emitted by a controller, a neural network with the fixed
structure trained using reinforcement learning. More crucially, we demonstrate
how to efficiently search for these architectures within limited time and
computational budgets. In particular, we rely on a progressive strategy that
terminates non-promising architectures from being further trained, and on
Polyak averaging coupled with knowledge distillation to speed-up the
convergence. Quantitatively, in 8 GPU-days our approach discovers a set of
architectures performing on-par with state-of-the-art among compact models on
the semantic segmentation, pose estimation and depth prediction tasks. Code
will be made available here: https://github.com/drsleep/nas-segm-pytorch
