Lightweight INRs Predict Error Distributions for Better Accuracy.

Researchers have introduced a lightweight method for Implicit Neural Representations (INRs) that allows them to predict not just values, but also the associated error distributions. INRs are compact encoders for volumes, but they are inherently lossy, leading to prediction errors. Traditional uncertainty estimation methods for INRs are often computationally intensive or rely on restrictive parametric assumptions like Gaussian distributions. The proposed approach tackles this by reframing the regression-based INR training as a classification task. This is achieved by discretizing continuous target values into bins, which enables the model to flexibly capture complex, multimodal predictive behaviors. The study demonstrates that this classification-based setting achieves both high reconstruction quality and competitive error awareness through uncertainty estimation, outperforming conventional regression-based methods. This innovation provides a more nuanced understanding of INR predictions without adding significant computational overhead.