Deep Convolutional Networks as shallow Gaussian Processes
Adrià Garriga-Alonso, Carl Edward Rasmussen, Laurence Aitchison
Abstract
We show that the output of a (residual) CNN with an appropriate prior over the weights and biases is a GP in the limit of infinitely many convolutional filters, extending similar results for dense networks. For a CNN, the equivalent kernel can be computed exactly and, unlike "deep kernels", has very few parameters: only the hyperparameters of the original CNN. Further, we show that this kernel has two properties that allow it to be computed efficiently; the cost of evaluating the kernel for a pair of images is similar to a single forward pass through the original CNN with only one filter per layer. The kernel equivalent to a 32-layer ResNet obtains 0.84% classification error on MNIST, a new record for GP with a comparable number of parameters.
BibTeX
@inproceedings{
garriga-alonso2018deep,
title={Deep Convolutional Networks as shallow Gaussian Processes},
author={Adrià Garriga-Alonso and Carl Edward Rasmussen and Laurence Aitchison},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=Bklfsi0cKm},
}