ProFeat: Unsupervised image clustering via progressive feature refinement

Abstract

Unsupervised image clustering is a chicken-and-egg problem that involves representation learning and clustering. To resolve the inter-dependency between them, many approaches that iteratively perform the two tasks have been proposed, but their accuracy is limited due to inaccurate intermediate representations and clusters. To overcome this, this paper proposes ProFeat, a novel iterative approach to unsupervised image clustering based on progressive feature refinement. To learn discriminative features for clustering while avoiding adversarial influence from inaccurate intermediate clusters, ProFeat rigorously divides representation learning and clustering by modeling a neural network for clustering as a composition of an embedding and a clustering function and introducing an auxiliary embedding function. ProFeat progressively refines representations using confident samples from intermediate clusters using an extended contrastive loss. This paper also proposes ensemble-based feature refinement for more robust clustering. Our experiments demonstrate that ProFeat achieves superior results compared to previous methods.