  Neural Architecture Search (NAS) can automatically design architectures for
deep neural networks (DNNs) and has become one of the hottest research topics
in the current machine learning community. However, NAS is often
computationally expensive because a large number of DNNs require to be trained
for obtaining performance during the search process. Performance predictors can
greatly alleviate the prohibitive cost of NAS by directly predicting the
performance of DNNs. However, building satisfactory performance predictors
highly depends on enough trained DNN architectures, which are difficult to
obtain in most scenarios. To solve this critical issue, we propose an effective
DNN architecture augmentation method named GIAug in this paper. Specifically,
we first propose a mechanism based on graph isomorphism, which has the merit of
efficiently generating a factorial of $\boldsymbol n$ (i.e., $\boldsymbol n!$)
diverse annotated architectures upon a single architecture having $\boldsymbol
n$ nodes. In addition, we also design a generic method to encode the
architectures into the form suitable to most prediction models. As a result,
GIAug can be flexibly utilized by various existing performance predictors-based
NAS algorithms. We perform extensive experiments on CIFAR-10 and ImageNet
benchmark datasets on small-, medium- and large-scale search space. The
experiments show that GIAug can significantly enhance the performance of most
state-of-the-art peer predictors. In addition, GIAug can save three magnitude
order of computation cost at most on ImageNet yet with similar performance when
compared with state-of-the-art NAS algorithms.
