Towards deep kernel machines
Julien Mairal
(INRIA Grenoble, France)
Abstract:
In this talk, we introduce a new image representation based
on a multilayer kernel machine. Unlike traditional kernel methods where
data representation is decoupled from the prediction task, we learn how
to shape the kernel with supervision. We proceed by first introducing
an unsupervised image model called convolutional kernel networks
(CKNs); then, we derive backpropagation rules to take advantage of
labeled training data. The resulting model is a new type of
convolutional neural network, where optimizing the filters at each
layer is equivalent to learning a linear subspace in a reproducing
kernel Hilbert space (RKHS).
We show that our method achieves
reasonably competitive performance for image classification on some
standard " deep learning " datasets such as CIFAR-10 and SVHN, and also
for image super-resolution, demonstrating the applicability of our
approach to a large variety of image-related tasks.