Single-Domain Generalized Predictor for Neural Architecture Search System
Lianbo Ma, Haidong Kang, Guo Yu, Qing Li, Qiang He
Abstract
Performance predictors are used to reduce architecture evaluation costs in neural architecture search, which however suffers from a large amount of budget consumption in annotating substantial architectures trained from scratch. Hence, how to leverage existing annotated architectures to train a generalized predictor to find the optimal architecture on unseen target search spaces becomes a new research topic. To solve this issue, we propose a Single-Domain Generalized Predictor (SDGP), which aims to make the predictor only trained on a single source search space but perform well on target search spaces. In meta-learning, we firstly adopt feature extractor in learning the domain-invariant features of the architectures. Then, a neural predictor is trained to map the architectures to the accuracy of the candidate architectures over the target domain simulated on the source search space. Moreover, a novel multi-head attention driven regularizer is designed to regulate the predictor to further improve the generalization ability of the predictor for the feature extractor. A series of experimental results have shown that the proposed predictor outperforms the state-of-the-art predictors in generalization and achieves significant performance gains in finding the optimal architectures with test error 2.40% on CIFAR-10 and 23.20% on ImageNet1k within 0.01 GPU days.