Guided Evolution for Neural Architecture Search

Understand

Neural Architecture Search (NAS) methods have been successfully applied to image tasks with excellent results.

• However, NAS methods are often complex and tend to converge to local minima as soon as generated architectures seem to yield good results.
• In this paper, we propose G-EA, a novel approach for guided evolutionary NAS.
• The rationale behind G-EA, is to explore the search space by generating and evaluating several architectures in each generation at initialization stage using a zero-proxy estimator, where only the highest-scoring network is trained and kept for the next generation.