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To discover powerful yet compact models is an important goal of neuralarchitecture search. Previous two-stage one-shot approaches are limited bysearch space with a fixed depth. It seems handy to include an additional skipconnection in the search space to make depths variable. However, it creates alarge range of perturbation during supernet training and it has difficultygiving a confident ranking for subnetworks. In this paper, we discover thatskip connections bring about significant feature inconsistency compared withother operations, which potentially degrades the supernet performance. Based onthis observation, we tackle the problem by imposing an equivariant learnablestabilizer to homogenize such disparities. Experiments show that our proposedstabilizer helps to improve the supernet's convergence as well as rankingperformance. With an evolutionary search backend that incorporates thestabilized supernet as an evaluator, we derive a family of state-of-the-artarchitectures, the SCARLET series of several depths, especially SCARLET-Aobtains 76.9% top-1 accuracy on ImageNet. Code is available athttps://github.com/xiaomi-automl/ScarletNAS.

SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search