Calibrating Deep Convolutional Gaussian Processes

2018-05-26 · Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone

The wide adoption of Convolutional Neural Networks (CNNs) in applications where decision-making under uncertainty is fundamental, has brought a great deal of attention to the ability of these models to accurately quantify the uncertainty in their predictions. Previous work on combining CNNs with Gaussian processes (GPs) has been developed under the assumption that the predictive probabilities of these models are well-calibrated. In this paper we show that, in fact, current combinations of CNNs and GPs are miscalibrated. We proposes a novel combination that considerably outperforms previous approaches on this aspect, while achieving state-of-the-art performance on image classification tasks.

📄 PDF Abstract BibTeX arXiv:1805.10522

Code (1)

GiaLacTRAN/convolutional_deep_gp_random_features tf

Tasks

Decision MakingDecision Making Under UncertaintyGaussian Processes General Classification image-classification Image Classification

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