Affine Map

Affine maps, transformations that preserve collinearity and ratios of distances, are a fundamental concept with applications across diverse fields. Current research focuses on leveraging affine transformations in deep learning for tasks like image registration and keypoint description, often employing novel architectures such as transformers and steerers to improve efficiency and accuracy. Furthermore, investigations into the topological effects of affine maps on data manifolds are revealing insights into neural network behavior and data representation. These advancements have significant implications for various applications, including medical image analysis, robotics (SLAM), and machine learning model interpretability.