Bayesian neural networks (BNNs) and deep ensembles are the primary approaches for estimating predictive uncertainty in deep learning models.
However, the high cost of storage and inference makes it of limited utility in industrial-scale real-time applications. The ability of the model to quantify the distance of test examples from input space training data is a requirement for DNN to achieve high quality (ie, minimax optimal) uncertainty estimates. With a variety of vision and language
comprehension tasks, and the latest architectures (Wide-ResNet and BERT), SNGP with deep ensembles is competitive in out-domain prediction, calibration, and detection, and another single model. Better than the approach.
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