  Image classification with deep neural networks has seen a surge of
technological breakthroughs with promising applications in areas such as face
recognition, medical imaging, and autonomous driving. In engineering problems,
however, such as high-speed imaging of engine fuel injector sprays or body
paint sprays, deep neural networks face a fundamental challenge related to the
availability of adequate and diverse data. Typically, only thousands or
sometimes even hundreds of samples are available for training. In addition, the
transition between different spray classes is a continuum and requires a high
level of domain expertise to label the images accurately. In this work, we used
Mixup as an approach to systematically deal with the data scarcity and
ambiguous class boundaries found in industrial spray applications. We show that
data augmentation can mitigate the over-fitting problem of large neural
networks on small data sets, to a certain level, but cannot fundamentally
resolve the issue. We discuss how a convex linear interpolation of different
classes naturally aligns with the continuous transition between different
classes in our application. Our experiments demonstrate Mixup as a simple yet
effective method to train an accurate and robust deep neural network classifier
with only a few hundred samples.
