Unsupervised Learning Framework

Unsupervised learning frameworks aim to extract meaningful patterns and structures from unlabeled data, addressing the limitations of supervised methods that require extensive annotated datasets. Current research focuses on applying these frameworks to diverse problems, including image segmentation, time series analysis, combinatorial optimization, and anomaly detection, often employing neural networks, particularly graph neural networks and autoencoders, alongside techniques like contrastive learning and optimal transport. These advancements are significant because they enable the analysis of large, unlabeled datasets across various domains, leading to improved model generalization and potentially unlocking insights from data previously inaccessible to supervised learning approaches.