  Finding mines in Sonar imagery is a significant problem with a great deal of
relevance for seafaring military and commercial endeavors. Unfortunately, the
lack of enormous Sonar image data sets has prevented automatic target
recognition (ATR) algorithms from some of the same advances seen in other
computer vision fields. Namely, the boom in convolutional neural nets (CNNs)
which have been able to achieve incredible results - even surpassing human
actors - has not been an easily feasible route for many practitioners of Sonar
ATR. We demonstrate the power of one avenue to incorporating CNNs into Sonar
ATR: transfer learning. We first show how well a straightforward, flexible CNN
feature-extraction strategy can be used to obtain impressive if not
state-of-the-art results. Secondly, we propose a way to utilize the powerful
transfer learning approach towards multiple instance target detection and
identification within a provided synthetic aperture Sonar data set.
