6 citations · 7 across the 3 of their papers we have counts for
4 papers
Rethinking Deep Clustering Paradigms: Self-Supervision Is All You Need
Amal Shaheena, Nairouz Mrabahb, Riadh Ksantinia +1
The recent advances in deep clustering have been made possible by significant progress in self-supervised and pseudo-supervised learning. However, the trade-off between self-superv…
Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering
Nairouz Mrabah, Mohamed Bouguessa, Mohamed Fawzi Touati +1
Most recent graph clustering methods have resorted to Graph Auto-Encoders (GAEs) to perform joint clustering and embedding learning. However, two critical issues have been overlook…
Adversarial Deep Embedded Clustering: on a better trade-off between Feature Randomness and Feature Drift
Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini
Clustering using deep autoencoders has been thoroughly investigated in recent years. Current approaches rely on simultaneously learning embedded features and clustering the data po…
Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction
Nairouz Mrabah, Naimul Mefraz Khan, Riadh Ksantini +1
In unsupervised learning, there is no apparent straightforward cost function that can capture the significant factors of variations and similarities. Since natural systems have smo…