Hyperbolic Metric Learning

Hyperbolic metric learning aims to represent data in hyperbolic space, leveraging its inherent hierarchical structure to improve performance on tasks like image retrieval and classification compared to traditional Euclidean methods. Current research focuses on developing novel algorithms and loss functions, often incorporating contrastive learning and uncertainty estimation within hyperbolic neural networks, to enhance model robustness and accuracy. This approach shows promise in addressing challenges such as outlier detection, few-shot learning, and class-incremental learning, particularly for data with complex hierarchical relationships, leading to improved performance in various computer vision applications.