  Neural network-based semantic segmentation has achieved remarkable results
when large amounts of annotated data are available, that is, in the supervised
case. However, such data is expensive to collect and so methods have been
developed to adapt models trained on related, often synthetic data for which
labels are readily available. Current adaptation approaches do not consider the
dependence of the generalization/transferability of these models on network
architecture. In this paper, we perform neural architecture search (NAS) to
provide architecture-level perspective and analysis for domain adaptation. We
identify the optimization gap that exists when searching architectures for
unsupervised domain adaptation which makes this NAS problem uniquely difficult.
We propose bridging this gap by using maximum mean discrepancy and regional
weighted entropy to estimate the accuracy metric. Experimental results on
several widely adopted benchmarks show that our proposed AutoAdapt framework
indeed discovers architectures that improve the performance of a number of
existing adaptation techniques.
