Efficient Neural Architecture Design via Capturing Architecture-Performance Joint Distribution

The relationship between architecture and performance is critical for improving the efficiency of neural architecture design, yet few efforts have been devoted to understanding this relationship between architecture and performance, especially architecture-performance joint distribution. In this paper, we propose Semi-Supervised Generative Adversarial Networks Neural Architecture Design Method or SemiGAN-NAD to capture the architecture-performance joint distribution with few performance labels. It is composed of Bidirectional Transformer of Architecture and Performance (Bi-Arch2Perf) and Neural Architecture Conditional Generation (NACG). Bi-Arch2Perf is developed to learn the joint distribution of architecture and performance from bidirectional conditional distribution through the adversarial training of the discriminator, the architecture generator, and the performance predictor. Then, the incorporation of semi-supervised learning optimizes the construction of Bi-Arch2Perf by utilizing a large amount of architecture information without performance annotation in search space. Based on the learned bidirectional relationship, the performance of architecture is predicted by NACG in high-performance architecture space to efficiently discover well-promising neural architectures. The experimental results on NAS benchmarks demonstrate that SemiGAN-NAD achieves competitive performance with reduced evaluation time compared with the latest NAS methods. Moreover, the high-performance architecture signatures learned by Bi-Arch2Perf are also illustrated in our experiments.