We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure. The model is a principled Bayesian framework for detecting hierarchical combinations of local features for image classification. We demonstrate greatly improved image classification performance compared to current Gaussian process approaches on the MNIST and CIFAR-10 datasets. In particular, we improve CIFAR-10 accuracy by over 10 percentage points.
@article{arxiv.1810.03052,
title = {Deep convolutional Gaussian processes},
author = {Kenneth Blomqvist and Samuel Kaski and Markus Heinonen},
journal= {arXiv preprint arXiv:1810.03052},
year = {2018}
}