  We revisit the one-shot Neural Architecture Search (NAS) paradigm and analyze
its advantages over existing NAS approaches. Existing one-shot method, however,
is hard to train and not yet effective on large scale datasets like ImageNet.
This work propose a Single Path One-Shot model to address the challenge in the
training. Our central idea is to construct a simplified supernet, where all
architectures are single paths so that weight co-adaption problem is
alleviated. Training is performed by uniform path sampling. All architectures
(and their weights) are trained fully and equally.
  Comprehensive experiments verify that our approach is flexible and effective.
It is easy to train and fast to search. It effortlessly supports complex search
spaces (e.g., building blocks, channel, mixed-precision quantization) and
different search constraints (e.g., FLOPs, latency). It is thus convenient to
use for various needs. It achieves start-of-the-art performance on the large
dataset ImageNet.
