Affine Transformation

Affine transformations, mathematical mappings that involve scaling, rotation, shearing, and translation, are central to many areas of computer vision, machine learning, and signal processing. Current research focuses on leveraging affine transformations to improve the robustness and efficiency of various models, including generative adversarial networks (GANs), large language models (LLMs), and convolutional neural networks (CNNs), often through techniques like contrastive learning and post-training quantization. These advancements enhance model performance in tasks such as image inpainting, object pose refinement, and robust feature extraction, impacting fields ranging from robotics and remote sensing to natural language processing and medical imaging.