mlsanityData to 7 Oct 2026

MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders

arXiv 2106.11914 ↗First posted 7 Jun 2021cs.NE cs.LGcited by 4

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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.

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

Builds on

20 of 37 references.

References · 20

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  4. 2018The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networkscited by 2,250
  5. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  6. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 902
  7. 2014Deep Learning in Neural Networks: An Overviewcited by 765
  8. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  9. 2019AutoML: A Survey of the State-of-the-Artcited by 388
  10. 2015Recent Advances in Convolutional Neural Networkscited by 360
  11. 2020Large image datasets: A pyrrhic win for computer vision?cited by 237
  12. 2016Generating Images with Perceptual Similarity Metrics based on Deep Networkscited by 221
8 more
  1. 2017Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localizationcited by 207
  2. 2019AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Losscited by 189
  3. 2017Evolving Deep Neural Networkscited by 93
  4. 2016Medical image denoising using convolutional denoising autoencoderscited by 76
  5. 2020Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?cited by 58
  6. 2017Autoencoder-augmented Neuroevolution for Visual Doom Playingcited by 8
  7. 2019NASNet: A Neuron Attention Stage-by-Stage Net for Single Image Derainingcited by 3
  8. 2020Complexity-based speciation and genotype representation for neuroevolutioncited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 0; 2026: 1.

4 papers.

  1. 2023AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detectioncited by 3
  2. 2021Fast and scalable neuroevolution deep learning architecture search for multivariate anomaly detectioncited by 2
  3. 2024AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection frameworkcited by 0
  4. 2026Evolutionary fine tuning of quantized convolution-based deep learning modelscited by 0