  The wide application of pre-trained models is driving the trend of
once-for-all training in one-shot neural architecture search (NAS). However,
training within a huge sample space damages the performance of individual
subnets and requires much computation to search for an optimal model. In this
paper, we present PreNAS, a search-free NAS approach that accentuates target
models in one-shot training. Specifically, the sample space is dramatically
reduced in advance by a zero-cost selector, and weight-sharing one-shot
training is performed on the preferred architectures to alleviate update
conflicts. Extensive experiments have demonstrated that PreNAS consistently
outperforms state-of-the-art one-shot NAS competitors for both Vision
Transformer and convolutional architectures, and importantly, enables instant
specialization with zero search cost. Our code is available at
https://github.com/tinyvision/PreNAS.
