Representations for Low-Budget Synthetic Aperture Radar (SAR) Active Learning
Abstract
Active learning (AL) seeks to minimize annotation costs by selecting the most useful training data for deep machine learning models. However, until recently, the most successful active learning algorithms have required a large initial labeled set to work properly. This makes them impractical for labor-intensive labeling tasks such as object detection and modalities such as synthetic aperture radar (SAR). Improvements in extracting useful intrinsic features from unlabeled data through self-supervised learning have reduced the need for large amounts of labeled data. As a result, AL can focus on selecting a smaller subset of the best data to label for supervised learning on downstream tasks. Since annotation costs tend to be higher for these downstream tasks, it is crucial that the AL algorithm perform well under low annotation budgets. In this work, we study feature representations for AL in the low-budget regime. Our AL approach builds upon existing self-supervised representation learning methods by leveraging features learned from pretrained models and applying them to the SAR modality. Using these self-supervised learned features, we employ a diversity-based sampling strategy to select the examples for labeling under a low annotation budget. Qualitative results indicate that self-supervision with datasets well-matched to the target domain, when combined with cluster sampling-based AL algorithms, can lead to the selection of diverse samples.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 01, 2023
- Accession Number
- AD1215103
Entities
People
- Jonathan K. Sato
- Julian Y. Raheema
- Martin T. Jaszewski
Organizations
- Naval Information Warfare Center Pacific