2019/05/04 by Martin Wistuba, Wistuba, Martin, Ambrish Rawat +3 · 1 voice · 6 citations
Computer Science · Mathematics · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Neural and Evolutionary Computing (cs.NE) #Reinforcement Learning in Robotics #cs.CV #cs.LG #cs.NE #stat.ML
paper · pdf · doi:10.48550/arxiv.1905.01392
openalex publication_date 2019/05/04 · arxiv published 2019/05/04 · arxiv updated 2019/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of a wide variety of automated methods for neural architecture search. The choice of the network architecture has proven to be critical, and many advances in deep learning spring from its immediate improvements. However, deep learning techniques are computationally intensive and their application requires a high level of domain knowledge. Therefore, even partial automation of this process helps to make deep learning more accessible to both researchers and practitioners. With this survey, we provide a formalism which unifies and categorizes the landscape of existing methods along with a detailed analysis that compares and contrasts the different approaches. We achieve this via a comprehensive discussion of the commonly adopted architecture search spaces and architecture optimization algorithms based on principles of reinforcement learning and evolutionary algorithms along with approaches that incorporate surrogate and one-shot models. Additionally, we address the new research directions which include constrained and multi-objective architecture search as well as automated data augmentation, optimizer and activation function search.