Geodesic Loss

Geodesic loss functions are increasingly used in machine learning to address the challenges of optimizing models whose outputs lie on non-Euclidean manifolds, such as rotation matrices or other group structures. Current research focuses on developing efficient algorithms that incorporate geodesic distances into the loss function, often within the context of deep neural networks, avoiding direct optimization of complex geodesic calculations. This approach improves the accuracy of various applications, including protein structure prediction, point cloud alignment, and head pose estimation, by better accounting for the inherent geometry of the data.