We present a novel type of neural fields that uses general radial bases for signal
representation. State-of-the-art neural fields typically rely on grid-based representations for …

Implicit neural representations (INRs) have emerged as a powerful paradigm in scene
reconstruction and computer graphics, showcasing remarkable results. By utilizing neural …

Implicit neural representations (INRs) have emerged as a promising approach for video
storage and processing showing remarkable versatility across various video tasks. However …

Implicit neural representation (INR) can describe the target scenes with high fidelity using a
small number of parameters, and is emerging as a promising data compression technique …

Digital terrain models (DTMs) are pivotal in remote sensing cartography and landscape
management requiring accurate surface representation and topological information …

In the field of medical image compression, Implicit Neural Representation (INR) networks
have shown remarkable versatility due to their flexible compression ratios, yet they are …

Emerging Implicit Neural Representation (INR) is a promising data compression technique,
which represents the data using the parameters of a Deep Neural Network (DNN). Existing …

Efficient storage and sharing of massive biomedical data would open up their wide
accessibility to different institutions and disciplines. However, compressors tailored for …

Implicit neural representation (INR) can describe the target scenes with high fidelity using a
small number of parameters, and is emerging as a promising data compression technique …

Despite their remarkable performance, deep neural networks remain mostly “black boxes”,
suggesting inexplicability and hindering their wide applications in fields requiring making …