# Deep Sparse Representation-based Classification

## TL;DR

This paper introduces a deep learning model combining autoencoders and sparse coding for improved classification, demonstrating superior results over traditional SRC methods across multiple datasets.

## Contribution

It proposes a novel deep transductive framework that integrates deep feature learning with sparse representation for classification.

## Key findings

- Outperforms state-of-the-art SRC methods on three datasets
- Learns robust deep features for sparse coding
- Provides open-source implementation for reproducibility

## Abstract

We present a transductive deep learning-based formulation for the sparse representation-based classification (SRC) method. The proposed network consists of a convolutional autoencoder along with a fully-connected layer. The role of the autoencoder network is to learn robust deep features for classification. On the other hand, the fully-connected layer, which is placed in between the encoder and the decoder networks, is responsible for finding the sparse representation. The estimated sparse codes are then used for classification. Various experiments on three different datasets show that the proposed network leads to sparse representations that give better classification results than state-of-the-art SRC methods. The source code is available at: github.com/mahdiabavisani/DSRC.