aw_nas: A Modularized and Extensible NAS framework

2020/11/25 by Xuefei Ning, Ning, Xuefei, Changcheng Tang +17
Computer Science · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE)

paper · pdf · doi:10.48550/arxiv.2012.10388

openalex publication_date 2020/11/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Neural Architecture Search (NAS) has received extensive attention due to its capability to discover neural network architectures in an automated manner. aw_nas is an open-source Python framework implementing various NAS algorithms in a modularized manner. Currently, aw_nas can be used to reproduce the results of mainstream NAS algorithms of various types. Also, due to the modularized design, one can simply experiment with different NAS algorithms for various applications with awnas (e.g., classification, detection, text modeling, fault tolerance, adversarial robustness, hardware efficiency, and etc.). Codes and documentation are available at https://github.com/walkerning/aw_nas.

Citations

• The Pascal Visual Object Classes (VOC) Challenge
• Learning Efficient Detector with Semi-supervised Adaptive Distillation
• Once-for-All: Train One Network and Specialize it for Efficient Deployment

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