Mirror Map

Mirror maps are mathematical transformations used to simplify complex constrained optimization problems by mapping a constrained space to an unconstrained one, allowing easier application of existing algorithms. Current research focuses on developing neural approximate mirror maps for diverse applications, including improving diffusion models for image generation and solving inverse problems, enhancing reinforcement learning algorithms like policy mirror descent, and improving 3D scene reconstruction by accurately modeling reflections in mirrors. This work has significant implications for various fields, enabling more efficient and accurate solutions to challenging problems in computer vision, machine learning, and optimization.