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.

本文提出了一种半监督评估神经架构的方法，使用自编码器发现神经架构的有意义的表示，并利用图卷积神经网络来预测它们的性能，以实现神经架构搜索的高效优化。我们在 NAS-Benchmark-101 数据集上进行了大量实验，证明了我们方法在减少寻找高效神经架构所需的完全训练的架构上是有效的。