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Neural architecture search (NAS) has brought significant progress in recentimage recognition tasks. Most existing NAS methods apply restricted searchspaces, which limits the upper-bound performance of searched models. To addressthis issue, we propose a new search space named MobileNet3-MT. By reducinghuman-prior knowledge in omni dimensions of networks, MobileNet3-MTaccommodates more potential candidates. For searching in this challengingsearch space, we present an efficient Multi-trial Evolution-based NAS methodtermed MENAS. Specifically, we accelerate the evolutionary search process bygradually pruning models in the population. Each model is trained with an earlystop and replaced by its Lottery Tickets (the explored optimal prunednetwork).In this way, the full training pipeline of cumbersome networks isprevented and more efficient networks are automatically generated. Extensiveexperimental results on ImageNet-1K, CIFAR-10, and CIFAR-100 demonstrate thatMENAS achieves state-of-the-art performance.

Multi-trial Neural Architecture Search with Lottery Tickets