Convolutional transform learning

Abstract

This work proposes a new representation learning technique called convolutional transform learning. In standard transform learning, a dense basis is learned that analyses the image to generate the representation from the image. Here, we learn a set of independent convolutional filters that operate on the images to produce representations (one corresponding to each filter). The major advantage of our proposed approach is that it is completely unsupervised; unlike CNNs where labeled images are required for training. Moreover, it relies on a well-sounded minimization technique with established convergence guarantees. We have compared the proposed method with dictionary learning and transform learning on standard image classification datasets. Results show that our method improves over the rest by a considerable margin.