Self-supervised learning has emerged as a powerful strategy to exploit vast quantities of unlabelled satellite and aerial imagery for tasks such as land-cover classification, object detection and ...
Self-supervised models generate implicit labels from unstructured data rather than relying on labeled datasets for supervisory signals. Self-supervised learning (SSL), a transformative subset of ...
Self-supervised learning has emerged as a powerful paradigm to bridge the gap between data abundance and label scarcity in medical imaging. By constructing supervisory signals from the data ...