Scalable Data Augmentation for Deep Learning

Scalable Data Augmentation (SDA) provides a framework for training deep neural networks (DNNs). Our methodology exploits auxiliary hidden units which are designed to avoid backtracking and traverse local modes in an efficient way. This allows us to exploit recent advantages in high performance computing such as scalable linear algebra (CUDA, XLA). We show how to implement standard activation and objective functions, including ReLU (Polson and Ročková, 2018), logit (Zhou et al., 2012) and SVM (Mallick et al., 2005) are all available as data augmentation schemes. Data augmentation strategies are commonplace in statistical applications such as EM, ECM and MM algorithms, as they accelerate convergence and can use Nesterov acceleration (Nesterov, 1983).