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The common pipeline of training deep neural networks consists of severalbuilding blocks such as data augmentation and network architecture selection.AutoML is a research field that aims at automatically designing those parts,but most methods explore each part independently because it is more challengingto simultaneously search all the parts. In this paper, we propose a jointoptimization method for data augmentation policies and network architectures tobring more automation to the design of training pipeline. The core idea of ourapproach is to make the whole part differentiable. The proposed method combinesdifferentiable methods for augmentation policy search and network architecturesearch to jointly optimize them in the end-to-end manner. The experimentalresults show our method achieves competitive or superior performance to theindependently searched results.

Joint Search of Data Augmentation Policies and Network Architectures