# Supervised Deep Sparse Coding Networks

arXiv: https://arxiv.org/abs/1701.08349 · HTML: https://mlsanity.com/paper/1701.08349/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2017.

## Built on by (most cited first, 2 of 2)

- [A Generalized Hierarchical Nonnegative Tensor Decomposition](https://mlsanity.com/paper/2109.14820/) (2021)
- [Neural Nonnegative Matrix Factorization for Hierarchical Multilayer Topic Modeling](https://mlsanity.com/paper/2303.00058/) (2023)

## Abstract (arXiv)

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.