An Introduction to Neural Architecture Search for Convolutional Networks
It serves as an introductory guide for newcomers to NAS, addressing the challenge of understanding major and emerging directions in the field.
The paper provides an introduction to Neural Architecture Search (NAS) for convolutional networks, explaining basic concepts and major advances in search spaces, algorithms, and evaluation techniques to help beginners navigate the rapidly growing field.
Neural Architecture Search (NAS) is a research field concerned with utilizing optimization algorithms to design optimal neural network architectures. There are many approaches concerning the architectural search spaces, optimization algorithms, as well as candidate architecture evaluation methods. As the field is growing at a continuously increasing pace, it is difficult for a beginner to discern between major, as well as emerging directions the field has followed. In this work, we provide an introduction to the basic concepts of NAS for convolutional networks, along with the major advances in search spaces, algorithms and evaluation techniques.