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With the rapid development of neural architecture search (NAS), researchersfound powerful network architectures for a wide range of vision tasks. However,it remains unclear if the searched architecture can transfer across differenttypes of tasks as manually designed ones did. This paper puts forward thisproblem, referred to as NAS in the wild, which explores the possibility offinding the optimal architecture in a proxy dataset and then deploying it tomostly unseen scenarios.  We instantiate this setting using a currently popular algorithm nameddifferentiable architecture search (DARTS), which often suffers unsatisfyingperformance while being transferred across different tasks. We argue that theaccuracy drop originates from the formulation that uses a super-network forsearch but a sub-network for re-training. The different properties of thesestages have resulted in a significant optimization gap, and consequently, thearchitectural parameters "over-fit" the super-network. To alleviate the gap, wepresent a progressive method that gradually increases the network depth duringthe search stage, which leads to the Progressive DARTS (P-DARTS) algorithm.With a reduced search cost (7 hours on a single GPU), P-DARTS achieves improvedperformance on both the proxy dataset (CIFAR10) and a few target problems(ImageNet classification, COCO detection and three ReID benchmarks). Our codeis available at \url{https://github.com/chenxin061/pdarts}.

Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild