# Unsupervised Feature Learning by Deep Sparse Coding

arXiv: https://arxiv.org/abs/1312.5783 · HTML: https://mlsanity.com/paper/1312.5783/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2013.

## Built on by (most cited first, 4 of 4)

- [Sparse Coding with Multi-Layer Decoders using Variance Regularization](https://mlsanity.com/paper/2112.09214/) (2021)
- [Brain Programming is Immune to Adversarial Attacks: Towards Accurate and Robust Image Classification using Symbolic Learning](https://mlsanity.com/paper/2103.01359/) (2021)
- [Exponential Lower Bounds for Threshold Circuits of Sub-Linear Depth and Energy](https://mlsanity.com/paper/2107.00223/) (2021)
- [Unlocking the Potential of Similarity Matching: Scalability, Supervision and Pre-training](https://mlsanity.com/paper/2308.02427/) (2023)

## Abstract (arXiv)

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.