  The common pipeline of training deep neural networks consists of several
building 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 challenging
to simultaneously search all the parts. In this paper, we propose a joint
optimization method for data augmentation policies and network architectures to
bring more automation to the design of training pipeline. The core idea of our
approach is to make the whole part differentiable. The proposed method combines
differentiable methods for augmentation policy search and network architecture
search to jointly optimize them in the end-to-end manner. The experimental
results show our method achieves competitive or superior performance to the
independently searched results.
