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Automated machine learning (AutoML) usually involves several crucialcomponents, such as Data Augmentation (DA) policy, Hyper-Parameter Optimization(HPO), and Neural Architecture Search (NAS). Although many strategies have beendeveloped for automating these components in separation, joint optimization ofthese components remains challenging due to the largely increased searchdimension and the variant input types of each component. In parallel to this,the common practice of searching for the optimal architecture first and thenretraining it before deployment in NAS often suffers from low performancecorrelation between the searching and retraining stages. An end-to-end solutionthat integrates the AutoML components and returns a ready-to-use model at theend of the search is desirable. In view of these, we propose DHA, whichachieves joint optimization of Data augmentation policy, Hyper-parameter andArchitecture. Specifically, end-to-end NAS is achieved in a differentiablemanner by optimizing a compressed lower-dimensional feature space, while DApolicy and HPO are regarded as dynamic schedulers, which adapt themselves tothe update of network parameters and network architecture at the same time.Experiments show that DHA achieves state-of-the-art (SOTA) results on variousdatasets and search spaces. To the best of our knowledge, we are the first toefficiently and jointly optimize DA policy, NAS, and HPO in an end-to-endmanner without retraining.

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