Transformation Matrix

Transformation matrices are fundamental tools in various fields, enabling the mapping and manipulation of data across different domains or perspectives. Current research focuses on developing efficient and adaptable transformation matrix learning methods, including those based on low-rank approximations, double transformation matrices, and generative adversarial networks, often within the context of deep learning architectures. These advancements improve performance in diverse applications such as few-shot learning, parameter-efficient fine-tuning of large language models, and computer vision tasks like gait recognition and tooth arrangement, ultimately leading to more robust and accurate models.