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Title: Deep Convolutional Networks as shallow Gaussian Processes

Paper content:
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.
Convolutional Neural Networks (CNNs) have powerful pattern-recognition capabilities that have recently given dramatic improvements in important tasks such as image classification BID13 .
However, as CNNs are increasingly being applied in real-world, safety-critical domains, their vulnerability to adversarial examples BID27 BID15 , and their poor uncertainty estimates are becoming increasingly problematic.
Bayesian inference is a theoretically principled and demonstrably successful BID26 BID7 framework for learning in the face of uncertainty, which may also help to address the problems of adversarial examples BID9 .
Unfortunately, Bayesian inference in CNNs is extremely difficult due to the very large number of parameters, requiring highly approximate factorised variational approximations BID1 BID8 , or requiring the storage BID16 ) of large numbers of posterior samples (Welling & Teh, 2011; BID19 .Other
methods such as those based on Gaussian Processes (GPs) are more amenable to Bayesian inference, allowing us to compute the posterior uncertainty exactly BID24 . This
raises the question of whether it might be possible to combine the pattern-recognition capabilities of CNNs with exact probabilistic computations in GPs. Two
such approaches exist in the literature. First
, deep convolutional kernels (Wilson et al., 2016 ) parameterise a GP kernel using the weights and biases of a CNN, which is used to embed the input images into some latent space before computing their similarity. The
CNN parameters of the resulting kernel then have to be optimised by gradient descent. However
, the large number of kernel parameters in the CNN reintroduces the risk of overconfidence and overfitting. To avoid
this risk, we need to infer a posterior over the CNN kernel parameters, which is as difficult as directly inferring a posterior over the parameters of the original CNN. Second,
it is possible to define a convolutional GP BID22 or a Furthermore, we show that two properties of the GP kernel induced by a CNN allow it to be computed very efficiently. First,
in previous work it was necessary to compute the covariance matrix for the output of a single convolutional filter applied at all possible locations within a single image BID22 , which was prohibitively computationally expensive. In contrast
, under our prior, the downstream weights are independent with zero-mean, which decorrelates the contribution from each location, and implies that it is necessary only to track the patch variances, and not their covariances. Second, while
it is still necessary to compute the variance of the output of a convolutional filter applied at all locations within the image, the specific structure of the kernel induced by the CNN means that the variance at every location can be computed simultaneously and efficiently as a convolution.Finally, we empirically demonstrate the performance increase coming from adding translationinvariant structure to the GP prior. Without computing
any gradients, and without augmenting the training set (e.g. using translations), we obtain 0.84% error rate on the MNIST classification benchmark, setting a new record for nonparametric GP-based methods.
Borovykh (2018) also suggests that a CNN exhibits GP behaviour.
However, they take the infinite limit with respect to the filter size, not the number of filters.
Thus, their infinite network is inapplicable to real data which is always of finite dimension.Finally, there is a series of papers analysing the mean-field behaviour of deep NNs and CNNs which aims to find good random initializations, i.e. those that do not exhibit vanishing or exploding gradients or activations BID25 Yang & Schoenholz, 2017) .
Apart from their very different focus, the key difference to our work is that they compute the variance for a single training-example, whereas to obtain the GPs kernel, we additionally need to compute the output covariances for different training/test examples (Xiao et al., 2018) .
We have shown that deep Bayesian CNNs with infinitely many filters are equivalent to a GP with a recursive kernel.
We also derived the kernel for the GP equivalent to a CNN, and showed that, in handwritten digit classification, it outperforms all previous GP approaches that do not incorporate a parametric NN into the kernel.
Given that most state-of-the-art neural networks incorporate structure (convolutional or otherwise) into their architecture, the equivalence between CNNs and GPs is potentially of considerable practical relevance.
In particular, we hope to apply GP CNNs in domains as widespread as adversarial examples, lifelong learning and k-shot learning, and we hope to improve them by developing efficient multi-layered inducing point approximation schemes.

assistant: text: We show that CNNs and ResNets with appropriate priors on the parameters are Gaussian processes in the limit of infinitely many convolutional filters.