Neural Architecture Search

Neural Architecture Search (NAS) automates network architecture engineering. It aims to learn a network topology that can achieve best performance on a certain task. Although most popular and successful model architectures are designed by human experts, it doesn't mean we have explored the entire network architecture space and settled down with the best option. We would have a better chance to find the optimal solution if we adopt a systematic and automatic way of learning high-performance model architectures. Automatically learning and evolving network topologies is not a new idea (Stanley & Miikkulainen, 2002). In recent years, the pioneering work by Zoph & Le 2017 and Baker et al. 2017 has attracted a lot of attention into the field of Neural Architecture Search (NAS), leading to many interesting ideas for better, faster and more cost-efficient NAS methods. As I started looking into NAS, I found this nice survey very helpful by Elsken, et al 2019. They characterize NAS as a system with three major components, which is clean & concise, and also commonly adopted in other NAS papers. The NAS search space defines a set of basic network operations and how operations can be connected to construct valid network architectures.