Self Supervised Approach

Self-supervised learning aims to train machine learning models using unlabeled data by creating surrogate supervised tasks, thereby reducing reliance on expensive and time-consuming data annotation. Current research focuses on applying this approach to diverse problems, including image classification, object detection, depth estimation, and anomaly detection, often employing transformer-based architectures, contrastive learning, and autoencoders. This methodology is significant because it enables the development of powerful models in domains with limited labeled data, impacting fields like medical imaging, robotics, and remote sensing through improved efficiency and broader applicability.