Unsupervised Clustering using Pseudo-semi-supervised Learning

Divam Gupta, Ramachandran Ramjee, Nipun Kwatra, Muthian Sivathanu

Keywords: Unsupervised Learning, Unsupervised Clustering, Deep Learning

TL;DR: Using ensembles and pseudo labels for unsupervised clustering

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.

Code: https://drive.google.com/open?id=1rvlTYnSDD9UVAy2FkKilM4fGSE75v7Id