# Training Stacked Denoising Autoencoders for Representation Learning

arXiv: https://arxiv.org/abs/2102.08012 · HTML: https://mlsanity.com/paper/2102.08012/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1312.6082/) (2013)
- [Genetic Algorithms for Evolving Deep Neural Networks](https://mlsanity.com/paper/1711.07655/) (2017)

## Built on by (most cited first, 1 of 1)

- [Improving Joint Embedding Predictive Architecture with Diffusion Noise](https://mlsanity.com/paper/2507.15216/) (2025)

## Abstract (arXiv)

We implement stacked denoising autoencoders, a class of neural networks that are capable of learning powerful representations of high dimensional data. We describe stochastic gradient descent for unsupervised training of autoencoders, as well as a novel genetic algorithm based approach that makes use of gradient information. We analyze the performance of both optimization algorithms and also the representation learning ability of the autoencoder when it is trained on standard image classification datasets.