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DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture
[article]
2022
arXiv
pre-print
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 optimization of these components remains challenging due to the largely increased search dimension and the variant input types of each component. In parallel to this, the common practice of searching for the
arXiv:2109.05765v2
fatcat:bkl76mhrbbbklo7ex25cn7bwzi