34,365 research outputs found
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks
In this paper, 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 proposing improvements of the
recently-introduced convolutional kernel networks (CKNs) in the context of
unsupervised learning; 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.Comment: to appear in Advances in Neural Information Processing Systems (NIPS
Subset Feature Learning for Fine-Grained Category Classification
Fine-grained categorisation has been a challenging problem due to small
inter-class variation, large intra-class variation and low number of training
images. We propose a learning system which first clusters visually similar
classes and then learns deep convolutional neural network features specific to
each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset
show that the proposed method outperforms recent fine-grained categorisation
methods under the most difficult setting: no bounding boxes are presented at
test time. It achieves a mean accuracy of 77.5%, compared to the previous best
performance of 73.2%. We also show that progressive transfer learning allows us
to first learn domain-generic features (for bird classification) which can then
be adapted to specific set of bird classes, yielding improvements in accuracy
- …