  Due to the expensive nature of field data gathering, the lack of training
data often limits the performance of Automatic Target Recognition (ATR)
systems. This problem is often addressed with domain adaptation techniques,
however the currently existing methods fail to satisfy the constraints of
resource and time-limited underwater systems. We propose to address this issue
via an online fine-tuning of the ATR algorithm using a novel data-selection
method. Our proposed data-mining approach relies on visual similarity and
outperforms the traditionally employed hard-mining methods. We present a
comparative performance analysis in a wide range of simulated environments and
highlight the benefits of using our method for the rapid adaptation to
previously unseen environments.
