DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture
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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. Meanwhile, conducting these components in a sequence often requires careful coordination by human experts and may lead to sub-optimal results. In parallel to this, the common practice of searching for the optimal architecture first and then retraining it before deployment in NAS often suffers from low performance correlation between the search and retraining stages. An end-to-end solution that integrates the AutoML components and returns a ready-to-use model at the end of the search is desirable. In view of these, we propose DHA, which achieves joint optimization of Data augmentation policy, Hyper-parameter and Architecture. Specifically, end-to-end NAS is achieved in a differentiable manner by optimizing a compressed lower-dimensional feature space, while DA policy and HPO are updated dynamically at the same time. Experiments show that DHA achieves state-of-the-art (SOTA) results on various datasets, especially 77.4% accuracy on ImageNet with cell based search space, which is higher than current SOTA by 0.5%. To the best of our knowledge, we are the first to efficiently and jointly optimize DA policy, NAS, and HPO in an end-to-end manner without retraining.
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