Compact Neural Representation

Compact neural representations aim to encode complex data, such as images, videos, and fMRI scans, using significantly fewer parameters than traditional methods, thereby improving storage efficiency and inference speed. Current research focuses on developing novel architectures like implicit neural representations (INRs) and coordinate networks, often incorporating techniques like multi-resolution decomposition, activation sharing, and physics-informed modeling to enhance both compactness and accuracy. These advancements are impacting diverse fields, enabling efficient storage and processing of large datasets in medical imaging, computer vision, and speech recognition, while also improving the speed and resource efficiency of real-time applications.