  In this paper, we propose a Customizable Architecture Search (CAS) approach
to automatically generate a network architecture for semantic image
segmentation. The generated network consists of a sequence of stacked
computation cells. A computation cell is represented as a directed acyclic
graph, in which each node is a hidden representation (i.e., feature map) and
each edge is associated with an operation (e.g., convolution and pooling),
which transforms data to a new layer. During the training, the CAS algorithm
explores the search space for an optimized computation cell to build a network.
The cells of the same type share one architecture but with different weights.
In real applications, however, an optimization may need to be conducted under
some constraints such as GPU time and model size. To this end, a cost
corresponding to the constraint will be assigned to each operation. When an
operation is selected during the search, its associated cost will be added to
the objective. As a result, our CAS is able to search an optimized architecture
with customized constraints. The approach has been thoroughly evaluated on
Cityscapes and CamVid datasets, and demonstrates superior performance over
several state-of-the-art techniques. More remarkably, our CAS achieves 72.3%
mIoU on the Cityscapes dataset with speed of 108 FPS on an Nvidia TitanXp GPU.
