Supervised Approach

Supervised approaches in machine learning aim to train models using labeled datasets to achieve accurate predictions on new, unseen data. Current research focuses on improving the efficiency and robustness of supervised learning, particularly by addressing challenges like limited labeled data (through techniques like pseudo-labeling and semi-supervised learning), handling distribution shifts, and quantifying model uncertainty. These advancements are crucial for various applications, including image segmentation, natural language processing, and anomaly detection, enabling more reliable and trustworthy AI systems across diverse domains.