  We propose Stochastic Neural Architecture Search (SNAS), an economical
end-to-end solution to Neural Architecture Search (NAS) that trains neural
operation parameters and architecture distribution parameters in same round of
back-propagation, while maintaining the completeness and differentiability of
the NAS pipeline. In this work, NAS is reformulated as an optimization problem
on parameters of a joint distribution for the search space in a cell. To
leverage the gradient information in generic differentiable loss for
architecture search, a novel search gradient is proposed. We prove that this
search gradient optimizes the same objective as reinforcement-learning-based
NAS, but assigns credits to structural decisions more efficiently. This credit
assignment is further augmented with locally decomposable reward to enforce a
resource-efficient constraint. In experiments on CIFAR-10, SNAS takes less
epochs to find a cell architecture with state-of-the-art accuracy than
non-differentiable evolution-based and reinforcement-learning-based NAS, which
is also transferable to ImageNet. It is also shown that child networks of SNAS
can maintain the validation accuracy in searching, with which attention-based
NAS requires parameter retraining to compete, exhibiting potentials to stride
towards efficient NAS on big datasets. We have released our implementation at
https://github.com/SNAS-Series/SNAS-Series.
