@inproceedings{gupta2020unsupervised,
author = {Gupta, Divam and Ramjee, Ramachandran and Kwatra, Nipun and Sivathanu, Muthian},
title = {Unsupervised Clustering using Pseudo-semi-supervised Learning},
booktitle = {Eighth International Conference on Learning Representations (ICLR)},
year = {2020},
month = {April},
abstract = {In this paper, we propose a framework that leverages semi-supervised models to improve unsupervised clustering performance. To leverage semi-supervised models, we first need to automatically generate labels, called pseudo-labels. We find that prior approaches for generating pseudo-labels hurt clustering performance because of their low accuracy. Instead, we use an ensemble of deep networks  to construct a similarity graph, from which we extract high accuracy pseudo-labels. The approach of finding high quality pseudo-labels using ensembles and training the semi-supervised model is iterated, yielding continued improvement. We show that our approach outperforms state of the art clustering results for multiple image and text datasets. For example, we achieve 54.6% accuracy for CIFAR-10 and 43.9% for 20news, outperforming state of the art by 8-12% in absolute terms.},
url = {http://approjects.co.za/?big=en-us/research/publication/unsupervised-clustering-using-pseudo-semi-supervised-learning/},
}