# Discriminative Autoencoder for Feature Extraction: Application to Character Recognition

arXiv: https://arxiv.org/abs/1912.12131 · HTML: https://mlsanity.com/paper/1912.12131/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [Deep Convolutional Autoencoders as Generic Feature Extractors in Seismological Applications](https://mlsanity.com/paper/2110.11802/) (2021)
- [Causal Autoencoder-like Generation of Feedback Fuzzy Cognitive Maps with an LLM Agent](https://mlsanity.com/paper/2509.25593/) (2025)
- [Analogue Forecast System for Daily Precipitation Prediction Using Autoencoder Feature Extraction: Application in Hong Kong](https://mlsanity.com/paper/2501.02814/) (2025)
- [Discrete Wavelet Transform as a Facilitator for Expressive Latent Space Representation in Variational Autoencoders in Satellite Imagery](https://mlsanity.com/paper/2510.00376/) (2025)
- [A self-supervised learning approach to deep filter banks for texture recognition](https://mlsanity.com/paper/2605.27843/) (2026)

## Abstract (arXiv)

Conventionally, autoencoders are unsupervised representation learning tools. In this work, we propose a novel discriminative autoencoder. Use of supervised discriminative learning ensures that the learned representation is robust to variations commonly encountered in image datasets. Using the basic discriminating autoencoder as a unit, we build a stacked architecture aimed at extracting relevant representation from the training data. The efficiency of our feature extraction algorithm ensures a high classification accuracy with even simple classification schemes like KNN (K-nearest neighbor). We demonstrate the superiority of our model for representation learning by conducting experiments on standard datasets for character/image recognition and subsequent comparison with existing supervised deep architectures like class sparse stacked autoencoder and discriminative deep belief network.