Hybrid Representation

Hybrid representation in machine learning combines the strengths of different representation methods, such as explicit (e.g., meshes, point clouds) and implicit (e.g., neural radiance fields) approaches, to improve the accuracy, efficiency, and generalizability of models across various domains. Current research focuses on developing novel hybrid architectures, often incorporating Gaussian splatting, tri-planes, or vector quantization, for tasks like 3D reconstruction, video processing, and scene understanding. These advancements are significantly impacting fields ranging from robotics and autonomous driving to medical imaging and music generation by enabling more accurate, efficient, and detailed representations of complex data.