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. 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:在现场重建和计算机图形方面,其表现主要是对重建准确性的评估,在现场重建和计算机图形方面已经取得了令人印象深刻的成果。随着IRS进入其他领域,模型预测为高决策提供了信息,对IRS推论的不确定性量化变得至关重要。为此,我们研究了在计算断层摄影方面对IRS(UncertaINR)进行巴伊西亚再版,并评估了巴伊西亚在精确性和校准方面的一些深层学习执行情况。我们发现,在与其他古典、IRS(IRI)和CNN(CNN)重建技术保持竞争力的同时,它们实现了精确的不确定性。 与以往最佳方法不同,UncertaINR并不需要大量的培训数据集,而只需要少量的验证图像。