# Self-Supervised Learning for Large-Scale Unsupervised Image Clustering Source: [Self-Supervised Learning for Large-Scale Unsupervised Image Clustering]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:16:11.686Z Authors: Evgenii Zheltonozhskii, Chaim Baskin, Alex M. Bronstein, Avi Mendelson Published: 2020-08-24T10:39:19.000Z ## Abstract Unsupervised learning has always been appealing to machine learning researchers and practitioners, allowing them to avoid an expensive and complicated process of labeling the data. However, unsupervised learning of complex data is challenging, and even the best approaches show much weaker performance than their supervised counterparts. Self-supervised deep learning has become a strong instrument for representation learning in computer vision. However, those methods have not been evaluated in a fully unsupervised setting. In this paper, we propose a simple scheme for unsupervised classification based on self-supervised representations. We evaluate the proposed approach with several recent self-supervised methods showing that it achieves competitive results for ImageNet classification (39% accuracy on ImageNet with 1000 clusters and 46% with overclustering). We suggest adding the unsupervised evaluation to a set of standard benchmarks for self-supervised learning. The code is available at https://github.com/Randl/kmeans\_selfsuper ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()