  Recent advances in the field of artificial intelligence have been made
possible by deep neural networks. In applications where data are scarce,
transfer learning and data augmentation techniques are commonly used to improve
the generalization of deep learning models. However, fine-tuning a transfer
model with data augmentation in the raw input space has a high computational
cost to run the full network for every augmented input. This is particularly
critical when large models are implemented on embedded devices with limited
computational and energy resources. In this work, we propose a method that
replaces the augmentation in the raw input space with an approximate one that
acts purely in the embedding space. Our experimental results show that the
proposed method drastically reduces the computation, while the accuracy of
models is negligibly compromised.
