Generalization bounds for deep convolutional neural networks

We prove bounds on the generalization error of convolutional networks.
The bounds are in terms of the training loss, the number of
parameters, the Lipschitz constant of the loss and the distance from
the weights to the initial weights. They are independent of the
number of pixels in the input, and the height and width of hidden
feature maps. We present experiments
with CIFAR-10 and a scaled-down variant, along with varying hyperparameters
of a deep convolutional network, comparing our bounds with practical
generalization gaps.

• Machine Intelligence