Evaluation Ranking is More Important for NAS
Yusen Zhang, Bao Li, Yusong Tan, Songlei Jian · 2022 International Joint Conference on Neural Networks (IJCNN) · 2022
Search space, searching method, and candidate evaluation scheme are critical to the success of Neural Architecture Search (NAS), especially the evaluation strategy. An effective and efficient neural architecture performance evaluator could successfully save computing costs and search time while guiding the NAS process to the optimal solution as fast as possible. Most existing NAS algorithms attempt to compute the absolute accuracy of the candidate architecture, which is almost impossible to achieve and meaningless to the final performance improvement. In this paper, we propose ERNAS, a novel neural architecture performance evaluation approach that optimizes the ranking of the candidate architecture performance, rather than the absolute accuracy itself. With the help of ERNAS, many existing NAS methods could achieve better performance without further evaluation. The experimental results demonstrate that ERNAS can be trained effectively enough with extremely limited training data (423 neural architectures randomly sampled form NAS-Bench-101, which is only 0.1% of the entire search space). The accuracy of the neural architecture search result produced by ERNAS is greater than that of the SOTA methods.