Supervised Deep Sparse Coding Networks
This work addresses the need for efficient and discriminative feature learning in computer vision, offering a novel deep network architecture with competitive results on benchmark datasets.
The paper tackles the problem of improving deep network performance with sparse coding by introducing the deep sparse coding network (SCN), which achieves 5.81% and 19.93% classification error rates on CIFAR-10 and CIFAR-100, respectively.
In this paper, we describe the deep sparse coding network (SCN), a novel deep network that encodes intermediate representations with nonnegative sparse coding. The SCN is built upon a number of cascading bottleneck modules, where each module consists of two sparse coding layers with relatively wide and slim dictionaries that are specialized to produce high dimensional discriminative features and low dimensional representations for clustering, respectively. During training, both the dictionaries and regularization parameters are optimized with an end-to-end supervised learning algorithm based on multilevel optimization. Effectiveness of an SCN with seven bottleneck modules is verified on several popular benchmark datasets. Remarkably, with few parameters to learn, our SCN achieves 5.81% and 19.93% classification error rate on CIFAR-10 and CIFAR-100, respectively.