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Neural architecture search (NAS) has shown great promise in designingstate-of-the-art (SOTA) models that are both accurate and efficient. Recently,two-stage NAS, e.g. BigNAS, decouples the model training and searching processand achieves remarkable search efficiency and accuracy. Two-stage NAS requiressampling from the search space during training, which directly impacts theaccuracy of the final searched models. While uniform sampling has been widelyused for its simplicity, it is agnostic of the model performance Pareto front,which is the main focus in the search process, and thus, misses opportunitiesto further improve the model accuracy. In this work, we propose AttentiveNASthat focuses on improving the sampling strategy to achieve better performancePareto. We also propose algorithms to efficiently and effectively identify thenetworks on the Pareto during training. Without extra re-training orpost-processing, we can simultaneously obtain a large number of networks acrossa wide range of FLOPs. Our discovered model family, AttentiveNAS models,achieves top-1 accuracy from 77.3% to 80.7% on ImageNet, and outperforms SOTAmodels, including BigNAS and Once-for-All networks. We also achieve ImageNetaccuracy of 80.1% with only 491 MFLOPs. Our training code and pretrained modelsare available at https://github.com/facebookresearch/AttentiveNAS.

AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling