@inproceedings{Forest2019,
abstract = {In the wake of recent advances in joint clustering and deep learning, we introduce the Deep Embedded Self-Organizing Map, a model that jointly learns representations and the code vectors of a self-organizing map. Our model is composed of an autoencoder and a custom SOM layer that are optimized in a joint training procedure, motivated by the idea that the SOM prior could help learning SOM-friendly representations. We eval- uate SOM-based models in terms of clustering quality and unsupervised clustering accuracy, and study the benefits of joint training.},
author = {Forest, Florent and Lebbah, Mustapha and Azzag, Hanane and Lacaille, J{\'{e}}r{\^{o}}me},
booktitle = {European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
keywords = {autoencoder,clustering,deep learning,representation learning,self-organizing map},
title = {{Deep Embedded SOM: Joint Representation Learning and Self-Organization}},
year = {2019},
url_Link = {https://www.esann.org/proceedings/2019},
url_Paper = {https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-30.pdf},
url_Slides = {ESANN-2019-DeepEmbeddedSOM-pres.pdf},
url_Code = {https://github.com/FlorentF9/DESOM}
}

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