Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models

• URL: http://arxiv.org/abs/2306.05272v5
• Date: Fri, 26 Apr 2024 14:10:49 GMT
• Title: Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models
• Authors: Tianzhe Chu, Shengbang Tong, Tianjiao Ding, Xili Dai, Benjamin David Haeffele, René Vidal, Yi Ma,
• Abstract summary: We propose a novel image clustering pipeline that leverages the powerful feature representation of large pre-trained models. We show that our pipeline works well on standard datasets such as CIFAR-10, CIFAR-100, and ImageNet-1k.
• Score: 37.574691902971296
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advent of large pre-trained models has brought about a paradigm shift in both visual representation learning and natural language processing. However, clustering unlabeled images, as a fundamental and classic machine learning problem, still lacks an effective solution, particularly for large-scale datasets. In this paper, we propose a novel image clustering pipeline that leverages the powerful feature representation of large pre-trained models such as CLIP and cluster images effectively and efficiently at scale. We first developed a novel algorithm to estimate the number of clusters in a given dataset. We then show that the pre-trained features are significantly more structured by further optimizing the rate reduction objective. The resulting features may significantly improve the clustering accuracy, e.g., from 57\% to 66\% on ImageNet-1k. Furthermore, by leveraging CLIP's multimodality bridge between image and text, we develop a simple yet effective self-labeling algorithm that produces meaningful captions for the clusters. Through extensive experiments, we show that our pipeline works well on standard datasets such as CIFAR-10, CIFAR-100, and ImageNet-1k. It also extends to datasets that are not curated for clustering, such as LAION-Aesthetics and WikiArts. We released the code in https://github.com/LeslieTrue/CPP.

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