Semi-supervised accuracy predictor-based multi-objective neural architecture search

The rise of neural architecture search (NAS) demonstrates the deep exploration between the neural network architecture and its performance (e.g., accuracy). Many NAS methods are inefficient because they train all candidates from scratch to obtain their accuracies. Although predictor-based NAS algorithms have been vigorously developed to efficiently and accurately evaluate the performance of candidate architectures, the training of accuracy predictors still require hundreds of architectures with ground truth. To overcome this shortcoming, this paper investigates an evolutionary-based NAS method, which constructs a semi-supervised accuracy predictor to efficiently and accurately evaluate candidate architectures. A one-time extractor and strong regressors are implemented to further enhance the prediction performance of the semi-supervised accuracy predictor. Furthermore, a multi-objective approach is developed to find architectures with high ground truth in a tradeoff between high prediction accuracy and prediction confidence. Experimental results demonstrate the strong competitiveness of the proposed approach on NAS benchmarks. The code is available at https://github.com/outofstyle/SAPMNAS.