  Neural architecture search (NAS) aims to automatically design deep neural
networks of satisfactory performance. Wherein, architecture performance
predictor is critical to efficiently value an intermediate neural architecture.
But for the training of this predictor, a number of neural architectures and
their corresponding real performance often have to be collected. In contrast
with classical performance predictor optimized in a fully supervised way, this
paper suggests a semi-supervised assessor of neural architectures. We employ an
auto-encoder to discover meaningful representations of neural architectures.
Taking each neural architecture as an individual instance in the search space,
we construct a graph to capture their intrinsic similarities, where both
labeled and unlabeled architectures are involved. A graph convolutional neural
network is introduced to predict the performance of architectures based on the
learned representations and their relation modeled by the graph. Extensive
experimental results on the NAS-Benchmark-101 dataset demonstrated that our
method is able to make a significant reduction on the required fully trained
architectures for finding efficient architectures.
