  A recent line of work showed that various forms of convolutional kernel
methods can be competitive with standard supervised deep convolutional networks
on datasets like CIFAR-10, obtaining accuracies in the range of 87-90% while
being more amenable to theoretical analysis. In this work, we highlight the
importance of a data-dependent feature extraction step that is key to the
obtain good performance in convolutional kernel methods. This step typically
corresponds to a whitened dictionary of patches, and gives rise to a
data-driven convolutional kernel methods. We extensively study its effect,
demonstrating it is the key ingredient for high performance of these methods.
Specifically, we show that one of the simplest instances of such kernel
methods, based on a single layer of image patches followed by a linear
classifier is already obtaining classification accuracies on CIFAR-10 in the
same range as previous more sophisticated convolutional kernel methods. We
scale this method to the challenging ImageNet dataset, showing such a simple
approach can exceed all existing non-learned representation methods. This is a
new baseline for object recognition without representation learning methods,
that initiates the investigation of convolutional kernel models on ImageNet. We
conduct experiments to analyze the dictionary that we used, our ablations
showing they exhibit low-dimensional properties.
