  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.
