Implicit neural representations (INRs) have achieved impressive results for scene reconstruction and computer graphics, where their performance has primarily been assessed on reconstruction accuracy. As INRs make their way into other domains, where model predictions inform high-stakes decision-making, uncertainty quantification of INR inference is becoming critical. To that end, we study a Bayesian reformulation of INRs, UncertaINR, in the context of computed tomography, and evaluate several Bayesian deep learning implementations in terms of accuracy and calibration. We find that they achieve well-calibrated uncertainty, while retaining accuracy competitive with other classical, INR-based, and CNN-based reconstruction techniques. Contrary to common intuition in the Bayesian deep learning literature, we find that INRs obtain the best calibration with computationally efficient Monte Carlo dropout, outperforming Hamiltonian Monte Carlo and deep ensembles. Moreover, in contrast to the best-performing prior approaches, UncertaINR does not require a large training dataset, but only a handful of validation images.

翻译：隐式神经表示在场景重建与计算机图形学领域取得了显著成果，其性能评估主要基于重建精度。随着隐式神经表示进入其他领域——其模型预测结果将影响高风险决策——隐式神经表示推理中的不确定性量化变得至关重要。为此，我们在计算机断层扫描背景下研究了隐式神经表示的贝叶斯重表述UncertaINR，并从精度与校准两个维度评估了多种贝叶斯深度学习实现。研究发现，这些方法在保持与经典重建技术、基于隐式神经表示的重建技术及基于CNN的重建技术相当的竞争力同时，能实现良好校准的不确定性。与贝叶斯深度学习文献中的普遍直觉相反，我们发现隐式神经表示通过计算高效的蒙特卡洛丢弃法获得最佳校准效果，其性能优于哈密顿蒙特卡洛和深度集成方法。此外，与先前最优方法相比，UncertaINR无需大规模训练数据集，仅需少量验证图像即可实现目标。