  Neural Architecture Search (NAS) has emerged as a key tool in identifying
optimal configurations of deep neural networks tailored to specific tasks.
However, training and assessing numerous architectures introduces considerable
computational overhead. One method to mitigating this is through performance
predictors, which offer a means to estimate the potential of an architecture
without exhaustive training. Given that neural architectures fundamentally
resemble Directed Acyclic Graphs (DAGs), Graph Neural Networks (GNNs) become an
apparent choice for such predictive tasks. Nevertheless, the scarcity of
training data can impact the precision of GNN-based predictors. To address
this, we introduce a novel GNN predictor for NAS. This predictor renders neural
architectures into vector representations by combining both the conventional
and inverse graph views. Additionally, we incorporate a customized training
loss within the GNN predictor to ensure efficient utilization of both types of
representations. We subsequently assessed our method through experiments on
benchmark datasets including NAS-Bench-101, NAS-Bench-201, and the DARTS search
space, with a training dataset ranging from 50 to 400 samples. Benchmarked
against leading GNN predictors, the experimental results showcase a significant
improvement in prediction accuracy, with a 3%--16% increase in Kendall-tau
correlation. Source codes are available at https://github.com/EMI-Group/fr-nas.
