  Recent advances in machine learning (ML) and computer vision tools have
enabled applications in a wide variety of arenas such as financial analytics,
medical diagnostics, and even within the Department of Defense. However, their
widespread implementation in real-world use cases poses several challenges: (1)
many applications are highly specialized, and hence operate in a \emph{sparse
data} domain; (2) ML tools are sensitive to their training sets and typically
require cumbersome, labor-intensive data collection and data labelling
processes; and (3) ML tools can be extremely "black box," offering users little
to no insight into the decision-making process or how new data might affect
prediction performance. To address these challenges, we have designed and
developed Data Augmentation from Proficient Pre-Training of Robust Generative
Adversarial Networks (DAPPER GAN), an ML analytics support tool that
automatically generates novel views of training images in order to improve
downstream classifier performance. DAPPER GAN leverages high-fidelity
embeddings generated by a StyleGAN2 model (trained on the LSUN cars dataset) to
create novel imagery for previously unseen classes. We experimentally evaluate
this technique on the Stanford Cars dataset, demonstrating improved vehicle
make and model classification accuracy and reduced requirements for real data
using our GAN based data augmentation framework. The method's validity was
supported through an analysis of classifier performance on both augmented and
non-augmented datasets, achieving comparable or better accuracy with up to 30\%
less real data across visually similar classes. To support this method, we
developed a novel augmentation method that can manipulate semantically
meaningful dimensions (e.g., orientation) of the target object in the embedding
space.
