Core Concepts
The author proposes the RoBoT algorithm to robustify and boost training-free NAS by combining metrics and exploiting a new metric to bridge the estimation gap, improving search performance.
Abstract
The paper introduces the RoBoT algorithm for Neural Architecture Search (NAS) optimization. It addresses challenges in training-free NAS by proposing a method to combine metrics and exploit a new metric to improve search performance. The theoretical guarantees of RoBoT's expected performance are discussed, along with insights into factors influencing its effectiveness. Extensive experiments on various NAS benchmarks validate the superior performance of RoBoT compared to existing methods.
Key Points:
- Introduction of RoBoT algorithm for NAS optimization.
- Proposal to combine metrics and exploit a new metric for improved search performance.
- Theoretical analysis providing insights into RoBoT's expected performance.
- Empirical validation through experiments on different NAS benchmarks.
Stats
Recently, several training-free metrics have emerged, aiming to estimate the generalization performance of neural architectures.
These advancements have been largely driven by sophisticated deep neural networks like AlexNet, VGG, and ResNet.
Several training-free metrics have been proposed to address the issue of significant computational resources required for performance estimation in training-based NAS algorithms.
The proposed RoBoT algorithm aims to develop a robust and consistently better-performing metric on diverse tasks using Bayesian optimization.
Precision @ T value is used as a measure to quantify the estimation gap between training-free metrics and true architecture performances.
Quotes
"To address these challenges, we propose the robustifying and boosting training-free NAS (RoBoT) algorithm."
"Our extensive experiments on various NAS benchmark tasks yield substantial empirical evidence to support our theoretical results."