DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture

• Published in: arXiv.org
• Main Authors: Zhou, Kaichen, Hong, Lanqing, Hu, Shoukang, Zhou, Fengwei, Binxin Ru, Feng, Jiashi, Li, Zhenguo
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 03.11.2022
• Subjects: Automation; Data augmentation; Machine learning; Optimization; Parameters; Searching

Automated machine learning (AutoML) usually involves several crucial components, such as Data Augmentation (DA) policy, Hyper-Parameter Optimization (HPO), and Neural Architecture Search (NAS). Although many strategies have been developed for automating these components in separation, joint optimiza...