English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The advent of large pre-trained models has brought about a paradigm shift inboth visual representation learning and natural language processing. However,clustering unlabeled images, as a fundamental and classic machine learningproblem, still lacks an effective solution, particularly for large-scaledatasets. In this paper, we propose a novel image clustering pipeline thatleverages the powerful feature representation of large pre-trained models suchas CLIP and cluster images effectively and efficiently at scale. We firstdeveloped a novel algorithm to estimate the number of clusters in a givendataset. We then show that the pre-trained features are significantly morestructured by further optimizing the rate reduction objective. The resultingfeatures may significantly improve the clustering accuracy, e.g., from 57% to66% on ImageNet-1k. Furthermore, by leveraging CLIP's multimodality bridgebetween image and text, we develop a simple yet effective self-labelingalgorithm that produces meaningful text labels for the clusters. Throughextensive experiments, we show that our pipeline works well on standarddatasets such as CIFAR-10, CIFAR-100, and ImageNet-1k. It also extends todatasets without predefined labels, such as LAION-Aesthetics and WikiArts. Wereleased the code in https://github.com/LeslieTrue/CPP.

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