Similarity-based data mining for online domain adaptation of a sonar ATR system
This addresses domain adaptation for resource-constrained underwater systems, though it appears incremental as it builds on existing fine-tuning and data-mining techniques.
The paper tackles the problem of limited training data for underwater Automatic Target Recognition systems by proposing an online fine-tuning approach with a novel similarity-based data selection method, which outperforms traditional hard-mining methods across various simulated environments.
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.