In this paper, we propose a new unsupervised feature learning framework, namely Deep Sparse Coding (DeepSC), that extends sparse coding to a multi-layer architecture for visual object recognition tasks. The main innovation of the framework is that it connects the sparse-encoders from different layers by a sparse-to-dense module. The sparse-to-dense module is a composition of a local spatial pooling step and a low-dimensional embedding process, which takes advantage of the spatial smoothness information in the image. As a result, the new method is able to learn several levels of sparse representation of the image which capture features at a variety of abstraction levels and simultaneously preserve the spatial smoothness between the neighboring image patches. Combining the feature representations from multiple layers, DeepSC achieves the state-of-the-art performance on multiple object recognition tasks.
@article{arxiv.1312.5783,
title = {Unsupervised Feature Learning by Deep Sparse Coding},
author = {Yunlong He and Koray Kavukcuoglu and Yun Wang and Arthur Szlam and Yanjun Qi},
journal= {arXiv preprint arXiv:1312.5783},
year = {2013}
}