# MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders

arXiv: https://arxiv.org/abs/2106.11914 · HTML: https://mlsanity.com/paper/2106.11914/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 20 of 20)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Large image datasets: A pyrrhic win for computer vision?](https://mlsanity.com/paper/2006.16923/) (2020)
- [Generating Images with Perceptual Similarity Metrics based on Deep Networks](https://mlsanity.com/paper/1602.02644/) (2016)
- [Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization](https://mlsanity.com/paper/1704.04232/) (2017)
- [AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss](https://mlsanity.com/paper/1905.05879/) (2019)
- [Evolving Deep Neural Networks](https://mlsanity.com/paper/1703.00548/) (2017)
- [Medical image denoising using convolutional denoising autoencoders](https://mlsanity.com/paper/1608.04667/) (2016)
- [Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?](https://mlsanity.com/paper/2006.06936/) (2020)
- [Autoencoder-augmented Neuroevolution for Visual Doom Playing](https://mlsanity.com/paper/1707.03902/) (2017)
- [NASNet: A Neuron Attention Stage-by-Stage Net for Single Image Deraining](https://mlsanity.com/paper/1912.03151/) (2019)
- [Complexity-based speciation and genotype representation for neuroevolution](https://mlsanity.com/paper/2010.05176/) (2020)

## Built on by (most cited first, 4 of 4)

- [AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection](https://mlsanity.com/paper/2305.16497/) (2023)
- [Fast and scalable neuroevolution deep learning architecture search for multivariate anomaly detection](https://mlsanity.com/paper/2112.05640/) (2021)
- [AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework](https://mlsanity.com/paper/2404.07968/) (2024)
- [Evolutionary fine tuning of quantized convolution-based deep learning models](https://mlsanity.com/paper/2605.05228/) (2026)

## Abstract (arXiv)

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.