  Neural Architecture Search (NAS), aiming at automatically designing network
architectures by machines, is hoped and expected to bring about a new
revolution in machine learning. Despite these high expectation, the
effectiveness and efficiency of existing NAS solutions are unclear, with some
recent works going so far as to suggest that many existing NAS solutions are no
better than random architecture selection. The inefficiency of NAS solutions
may be attributed to inaccurate architecture evaluation. Specifically, to speed
up NAS, recent works have proposed under-training different candidate
architectures in a large search space concurrently by using shared network
parameters; however, this has resulted in incorrect architecture ratings and
furthered the ineffectiveness of NAS.
  In this work, we propose to modularize the large search space of NAS into
blocks to ensure that the potential candidate architectures are fully trained;
this reduces the representation shift caused by the shared parameters and leads
to the correct rating of the candidates. Thanks to the block-wise search, we
can also evaluate all of the candidate architectures within a block. Moreover,
we find that the knowledge of a network model lies not only in the network
parameters but also in the network architecture. Therefore, we propose to
distill the neural architecture (DNA) knowledge from a teacher model as the
supervision to guide our block-wise architecture search, which significantly
improves the effectiveness of NAS. Remarkably, the capacity of our searched
architecture has exceeded the teacher model, demonstrating the practicability
and scalability of our method. Finally, our method achieves a state-of-the-art
78.4\% top-1 accuracy on ImageNet in a mobile setting, which is about a 2.1\%
gain over EfficientNet-B0. All of our searched models along with the evaluation
code are available online.
