Unsupervised Deep Learning

Unsupervised deep learning aims to extract meaningful patterns and representations from data without relying on labeled examples, a crucial aspect given the scarcity of labeled data in many domains. Current research focuses on applying deep neural networks, including autoencoders, convolutional neural networks, and normalizing flows, to diverse tasks such as image segmentation, anomaly detection (in areas like medical imaging, volcanic activity, and network security), and solving inverse problems. These advancements are significantly impacting various fields, enabling improved analysis in areas like healthcare (e.g., Alzheimer's detection, bone age assessment), communications (e.g., beam training), and geophysical imaging, while also offering new approaches to fundamental problems in computer vision and signal processing.