Unsupervised Loss Function

Unsupervised loss functions are crucial for training deep learning models in scenarios lacking labeled data, a common challenge across diverse fields. Current research focuses on developing novel loss functions that leverage inherent data structures (e.g., geometric relationships, temporal sequences) or utilize self-supervision techniques to guide model learning, often incorporating graph neural networks or recurrent neural networks within the overall architecture. These advancements enable improved performance in tasks like 3D reconstruction, object tracking, and anomaly detection, impacting various applications from autonomous systems to historical document analysis. The ultimate goal is to create robust and accurate models without the need for extensive manual annotation.