In unsupervised learning, there is no apparent straightforward cost function that can capture the significant factors of variations and similarities. Since natural systems have smooth dynamics, an opportunity is lost if an unsupervised objective function remains static. The absence of concrete supervision suggests that smooth dynamics should be integrated during the training process. Compared to classical static cost functions, dynamic objective functions allow to better make use of the gradual and uncertain knowledge acquired through pseudo-supervision. In this paper, we propose Dynamic Autoencoder (DynAE), a novel model for deep clustering that overcomes a clustering-reconstruction trade-off, by gradually and smoothly eliminating the reconstruction objective function in favor of a construction one. Experimental evaluations on benchmark datasets show that our approach achieves state-of-the-art results compared to the most relevant deep clustering methods.
You can run the code of DynAE on google Colaboratory plateform without installing any package.
- Create a folder entitled "Colab" in your google drive.
- Download the two folders "DynAE" and "data" and drop them in the Colab folder.
- Run the notebook "dynAE.ipynb" using Colab.
@article{mrabah2020deep,
title={Deep clustering with a dynamic autoencoder: From reconstruction towards centroids construction},
author={Mrabah, Nairouz and Khan, Naimul Mefraz and Ksantini, Riadh and Lachiri, Zied},
journal={Neural Networks},
volume={130},
pages={206--228},
year={2020},
publisher={Elsevier}
}