Unsupervised Image Classification with Adaptive Nearest Neighbor Selection and Cluster Ensembles
This addresses the problem of grouping unlabeled images into categories for computer vision applications, representing an incremental advance over existing multi-head clustering methods.
The paper tackles unsupervised image classification by introducing adaptive nearest neighbor selection and cluster ensembling to improve clustering performance, achieving state-of-the-art results including 99.3% accuracy on CIFAR10, 89% on CIFAR100, and 70.4% on ImageNet.
Unsupervised image classification, or image clustering, aims to group unlabeled images into semantically meaningful categories. Early methods integrated representation learning and clustering within an iterative framework. However, the rise of foundational models have recently shifted focus solely to clustering, bypassing the representation learning step. In this work, we build upon a recent multi-head clustering approach by introducing adaptive nearest neighbor selection and cluster ensembling strategies to improve clustering performance. Our method, "Image Clustering through Cluster Ensembles" (ICCE), begins with a clustering stage, where we train multiple clustering heads on a frozen backbone, producing diverse image clusterings. We then employ a cluster ensembling technique to consolidate these potentially conflicting results into a unified consensus clustering. Finally, we train an image classifier using the consensus clustering result as pseudo-labels. ICCE achieves state-of-the-art performance on ten image classification benchmarks, achieving 99.3% accuracy on CIFAR10, 89% on CIFAR100, and 70.4% on ImageNet datasets, narrowing the performance gap with supervised methods. To the best of our knowledge, ICCE is the first fully unsupervised image classification method to exceed 70% accuracy on ImageNet.