# Training Stacked Denoising Autoencoders for Representation Learning

Source: [Training Stacked Denoising Autoencoders for Representation Learning](<https://www.sota2.com/research/paper/68fa69491e209131cea21350>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:56:51.523Z

Authors: Jason Liang, Keith Kelly

Published: 2021-02-16T08:18:22.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2102.08012>)

[PDF](<https://arxiv.org/pdf/2102.08012.pdf>)