Abstract
Automated design of deep neural networks using performance predictor has become a hot topic in current research. Neural architecture search (NAS) methods can be used to enable automatic design of neural network structures by defining different search spaces, search strategies, or optimization strategies. Evolutionary computation by many researchers as the search strategy for NAS, which is called evolutionary NAS (ENAS) . However, ENAS is time-consuming in evaluating the performance of network structures, which hinders the development of ENAS. Therefore, predicting network architecture performance using performance predictor can improve ENAS search speed and save computational resources. This paper summarizes several ENAS methods that utilize performance predictor, and discusses an outlook on search space, search strategies, and the future directions of ENAS assisted by performance predictor.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Bulletin of Chinese Applied Mathematics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
