Unsupervised Feature Learning by Deep Sparse Coding
Abstract
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.
Cite
Text
He et al. "Unsupervised Feature Learning by Deep Sparse Coding." International Conference on Learning Representations, 2014. doi:10.1137/1.9781611973440.103Markdown
[He et al. "Unsupervised Feature Learning by Deep Sparse Coding." International Conference on Learning Representations, 2014.](https://mlanthology.org/iclr/2014/he2014iclr-unsupervised/) doi:10.1137/1.9781611973440.103BibTeX
@inproceedings{he2014iclr-unsupervised,
title = {{Unsupervised Feature Learning by Deep Sparse Coding}},
author = {He, Yunlong and Kavukcuoglu, Koray and Wang, Yun and Szlam, Arthur and Qi, Yanjun},
booktitle = {International Conference on Learning Representations},
year = {2014},
doi = {10.1137/1.9781611973440.103},
url = {https://mlanthology.org/iclr/2014/he2014iclr-unsupervised/}
}