# MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders

Source: [MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders](<https://www.sota2.com/research/paper/68fa6a3f1e209131cea27037>)

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

Data updated: 2025-10-25T23:20:37.043Z

Authors: Daniel Dimanov, Emili Balaguer-Ballester, Colin Singleton, Shahin Rostami

Published: 2021-06-07T13:24:08.000Z

## Abstract

In this paper, we present a novel neuroevolutionary method to identify the architecture and hyperparameters of convolutional autoencoders. Remarkably, we used a hypervolume indicator in the context of neural architecture search for autoencoders, for the first time to our current knowledge. Results show that images were compressed by a factor of more than 10, while still retaining enough information to achieve image classification for the majority of the tasks. Thus, this new approach can be used to speed up the AutoML pipeline for image compression.

## 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/2106.11914>)

[PDF](<https://arxiv.org/pdf/2106.11914.pdf>)