Efficient Neural Architecture Search (ENAS) is a method designed to automate the design of neural network architectures, making the process more efficient and less resource-intensive. Introduced by researchers at Google Brain, Carnegie Mellon, and Stanford, ENAS aims to significantly reduce the computational cost associated with traditional neural architecture search (NAS) methods by sharing parameters across different candidate architectures. Here is a detailed overview of ENAS:
1. Introduction
ENAS is designed to automate the creation of neural network architectures, allowing for the discovery of highly effective models without the need for extensive manual design and trial-and-error. ENAS leverages a controller network to search through a space of possible architectures efficiently by sharing weights across these architectures.
Key Motivation: Traditional NAS methods are computationally expensive and require substantial resources, as they typically train each candidate architecture from scratch. ENAS addresses this by using a parameter-sharing approach, where multiple architectures share weights during the search process, drastically reducing the time and computational resources needed.
2. Architecture and Mechanism
Controller Network: The core of ENAS is the controller network, which is typically a recurrent neural network (RNN). This controller generates the architecture of the child…
