Kernel Basis Network

Kernel Basis Networks (KBNs) leverage learned kernel functions to adaptively aggregate spatial information in various data types, improving upon the limitations of static convolutional kernels in convolutional neural networks (CNNs). Current research focuses on developing efficient KBN architectures for image restoration tasks (e.g., denoising, super-resolution) and extending their application to graph classification and video interpolation, often incorporating iterative refinement and multi-encoder strategies to enhance performance and reduce computational cost. This approach offers a powerful alternative to purely CNN or transformer-based methods, leading to improved accuracy and efficiency in diverse applications requiring spatial feature extraction and aggregation.