  Biology is both an important application area and a source of motivation for
development of advanced machine learning techniques. Although much attention
has been paid to large and complex data sets resulting from high-throughput
sequencing, advances in high-quality video recording technology have begun to
generate similarly rich data sets requiring sophisticated techniques from both
computer vision and time-series analysis. Moreover, just as studying gene
expression patterns in one organism can reveal general principles that apply to
other organisms, the study of complex social interactions in an experimentally
tractable model system, such as a laboratory ant colony, can provide general
principles about the dynamics of other social groups. Here, we focus on one
such example from the study of reproductive regulation in small laboratory
colonies of more than 50 Harpegnathos ants. These ants can be artificially
induced to begin a ~20 day process of hierarchy reformation. Although the
conclusion of this process is conspicuous to a human observer, it remains
unclear which behaviors during the transient period are contributing to the
process. To address this issue, we explore the potential application of
One-class Classification (OC) to the detection of abnormal states in ant
colonies for which behavioral data is only available for the normal societal
conditions during training. Specifically, we build upon the Deep Support Vector
Data Description (DSVDD) and introduce the Inner-Outlier Generator (IO-GEN)
that synthesizes fake "inner outlier" observations during training that are
near the center of the DSVDD data description. We show that IO-GEN increases
the reliability of the final OC classifier relative to other DSVDD baselines.
This method can be used to screen video frames for which additional human
observation is needed.
