  In this work, we present an in-depth and systematic analysis using tools such
as local interpretable model-agnostic explanations (LIME) (arXiv:1602.04938)
and divergence measures to analyze what changes lead to improvement in
performance in fine tuned models for synthetic aperture sonar (SAS) data. We
examine the sensitivity to factors in the fine tuning process such as class
imbalance. Our findings show not only an improvement in seafloor texture
classification, but also provide greater insight into what features play
critical roles in improving performance as well as a knowledge of the
importance of balanced data for fine tuning deep learning models for seafloor
classification in SAS imagery.
