  One-Shot Neural architecture search (NAS) attracts broad attention recently
due to its capacity to reduce the computational hours through weight sharing.
However, extensive experiments on several recent works show that there is no
positive correlation between the validation accuracy with inherited weights
from the supernet and the test accuracy after re-training for One-Shot NAS.
Different from devising a controller to find the best performing architecture
with inherited weights, this paper focuses on how to sample architectures to
train the supernet to make it more predictive. A single-path supernet is
adopted, where only a small part of weights are optimized in each step, to
reduce the memory demand greatly. Furthermore, we abandon devising complicated
reward based architecture sampling controller, and sample architectures to
train supernet based on novelty search. An efficient novelty search method for
NAS is devised in this paper, and extensive experiments demonstrate the
effectiveness and efficiency of our novelty search based architecture sampling
method. The best architecture obtained by our algorithm with the same search
space achieves the state-of-the-art test error rate of 2.51\% on CIFAR-10 with
only 7.5 hours search time in a single GPU, and a validation perplexity of
60.02 and a test perplexity of 57.36 on PTB. We also transfer these search cell
structures to larger datasets ImageNet and WikiText-2, respectively.
