HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural Representations via Knowledge Distillation

Understand

Implicit Neural Representations (INRs) have recently exhibited immense potential in the field of scientific visualization for both data generation and visualization tasks.

• However, these representations often consist of large multi-layer perceptrons (MLPs), necessitating millions of operations for a single forward pass, consequently hindering interactive visual exploration.
• While reducing the size of the MLPs and employing efficient parametric encoding schemes can alleviate this issue, it compromises generalizability for unseen parameters, rendering it unsuitable for tasks such as temporal super-resolution.
• In this paper, we introduce HyperINR, a novel hypernetwork architecture capable of directly predicting the weights for a compact INR.