  Neural architecture search (NAS) has shown promising results discovering
models that are both accurate and fast. For NAS, training a one-shot model has
become a popular strategy to rank the relative quality of different
architectures (child models) using a single set of shared weights. However,
while one-shot model weights can effectively rank different network
architectures, the absolute accuracies from these shared weights are typically
far below those obtained from stand-alone training. To compensate, existing
methods assume that the weights must be retrained, finetuned, or otherwise
post-processed after the search is completed. These steps significantly
increase the compute requirements and complexity of the architecture search and
model deployment. In this work, we propose BigNAS, an approach that challenges
the conventional wisdom that post-processing of the weights is necessary to get
good prediction accuracies. Without extra retraining or post-processing steps,
we are able to train a single set of shared weights on ImageNet and use these
weights to obtain child models whose sizes range from 200 to 1000 MFLOPs. Our
discovered model family, BigNASModels, achieve top-1 accuracies ranging from
76.5% to 80.9%, surpassing state-of-the-art models in this range including
EfficientNets and Once-for-All networks without extra retraining or
post-processing. We present ablative study and analysis to further understand
the proposed BigNASModels.
