Papers › SCAN: Learning to Classify Images without Labels

SCAN: Learning to Classify Images without Labels

25 May 2020ECCV 2020 8arXiv:2005.12320archive 2025-07-28

Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, Luc van Gool

Can we automatically group images into semantically meaningful clusters when ground-truth annotations are absent? The task of unsupervised image classification remains an important, and open challenge in computer vision. Several recent approaches have tried to tackle this problem in an end-to-end fashion. In this paper, we deviate from recent works, and advocate a two-step approach where feature learning and clustering are decoupled. First, a self-supervised task from representation learning is employed to obtain semantically meaningful features. Second, we use the obtained features as a prior in a learnable clustering approach. In doing so, we remove the ability for cluster learning to depend on low-level features, which is present in current end-to-end learning approaches. Experimental evaluation shows that we outperform state-of-the-art methods by large margins, in particular +26.6% on CIFAR10, +25.0% on CIFAR100-20 and +21.3% on STL10 in terms of classification accuracy. Furthermore, our method is the first to perform well on a large-scale dataset for image classification. In particular, we obtain promising results on ImageNet, and outperform several semi-supervised learning methods in the low-data regime without the use of any ground-truth annotations. The code is made publicly available at https://github.com/wvangansbeke/Unsupervised-Classification.

Paper PDF Conference PDF Code

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

Code

wvangansbeke/Unsupervised-Classification officialmentioned in papermentioned on GitHubpytorch report

2023-MindSpore-4/Code14 mindspore report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Classification Clustering General Classification Image Classification Image Clustering Representation Learning Semi-Supervised Image Classification Unsupervised Image Classification image-classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Image Clustering	CIFAR-10	SCAN	ARI	0.772	#18 of 40	Archive leaderboard	report
Image Clustering	CIFAR-10	SCAN	Accuracy	0.883	#18 of 40	Archive leaderboard	report
Image Clustering	CIFAR-10	SCAN	Backbone	ResNet-18	#18 of 40	Archive leaderboard	report
Image Clustering	CIFAR-10	SCAN	NMI	0.797	#18 of 40	Archive leaderboard	report
Image Clustering	CIFAR-10	SCAN	Train set	Train	#18 of 40	Archive leaderboard	report
Image Clustering	CIFAR-10	SCAN (Avg)	ARI	0.758	#19 of 40	Archive leaderboard	report
Image Clustering	CIFAR-10	SCAN (Avg)	Accuracy	0.876	#19 of 40	Archive leaderboard	report
Image Clustering	CIFAR-10	SCAN (Avg)	Backbone	ResNet-18	#19 of 40	Archive leaderboard	report
Image Clustering	CIFAR-10	SCAN (Avg)	NMI	0.787	#19 of 40	Archive leaderboard	report
Image Clustering	CIFAR-10	SCAN (Avg)	Train set	Train	#19 of 40	Archive leaderboard	report
Image Clustering	CIFAR-100	SCAN	ARI	0.333	#13 of 30	Archive leaderboard	report
Image Clustering	CIFAR-100	SCAN	Accuracy	0.507	#13 of 30	Archive leaderboard	report
Image Clustering	CIFAR-100	SCAN	NMI	0.486	#13 of 30	Archive leaderboard	report
Image Clustering	CIFAR-100	SCAN	Train Set	Train	#13 of 30	Archive leaderboard	report
Image Clustering	CIFAR-100	SCAN (Avg)	ARI	0.301	#16 of 30	Archive leaderboard	report
Image Clustering	CIFAR-100	SCAN (Avg)	Accuracy	0.459	#16 of 30	Archive leaderboard	report
Image Clustering	CIFAR-100	SCAN (Avg)	NMI	0.468	#16 of 30	Archive leaderboard	report
Image Clustering	CIFAR-100	SCAN (Avg)	Train Set	Train	#16 of 30	Archive leaderboard	report
Image Clustering	ImageNet	SCAN	Accuracy	39.9	#11 of 12	Archive leaderboard	report
Image Clustering	ImageNet	SCAN	NMI	72.0	#11 of 12	Archive leaderboard	report
Image Clustering	ImageNet-100 (TEMI Split)	SCAN	ACCURACY	0.662	#5 of 5	Archive leaderboard	report
Image Clustering	ImageNet-100 (TEMI Split)	SCAN	ARI	0.544	#5 of 5	Archive leaderboard	report
Image Clustering	ImageNet-100 (TEMI Split)	SCAN	NMI	0.787	#5 of 5	Archive leaderboard	report
Image Clustering	ImageNet-200	SCAN	ACCURACY	0.563	#4 of 5	Archive leaderboard	report
Image Clustering	ImageNet-200	SCAN	ARI	0.441	#4 of 5	Archive leaderboard	report
Image Clustering	ImageNet-200	SCAN	NMI	0.757	#4 of 5	Archive leaderboard	report
Image Clustering	ImageNet-50 (TEMI Split)	SCAN	ACCURACY	0.751	#5 of 5	Archive leaderboard	report
Image Clustering	ImageNet-50 (TEMI Split)	SCAN	ARI	0.635	#5 of 5	Archive leaderboard	report
Image Clustering	ImageNet-50 (TEMI Split)	SCAN	NMI	0.805	#5 of 5	Archive leaderboard	report
Image Clustering	STL-10	SCAN	Accuracy	0.809	#15 of 29	Archive leaderboard	report
Image Clustering	STL-10	SCAN	Backbone	ResNet-18	#15 of 29	Archive leaderboard	report
Image Clustering	STL-10	SCAN	NMI	0.698	#15 of 29	Archive leaderboard	report
Image Clustering	STL-10	SCAN	Train Split	Train	#15 of 29	Archive leaderboard	report
Image Clustering	STL-10	SCAN (Avg)	Accuracy	0.767	#16 of 29	Archive leaderboard	report
Image Clustering	STL-10	SCAN (Avg)	Backbone	ResNet-18	#16 of 29	Archive leaderboard	report
Image Clustering	STL-10	SCAN (Avg)	NMI	0.680	#16 of 29	Archive leaderboard	report
Image Clustering	STL-10	SCAN (Avg)	Train Split	Train	#16 of 29	Archive leaderboard	report
Semi-Supervised Image Classification	ImageNet - 1% labeled data	SCAN (ResNet-50\|Unsupervised)	Top 1 Accuracy	39.90%	#53 of 65	Archive leaderboard	report
Semi-Supervised Image Classification	ImageNet - 1% labeled data	SCAN (ResNet-50\|Unsupervised)	Top 5 Accuracy	60.0%	#53 of 65	Archive leaderboard	report
Unsupervised Image Classification	CIFAR-10	SCAN	Accuracy	88.3	#5 of 9	Archive leaderboard	report
Unsupervised Image Classification	CIFAR-20	SCAN	Accuracy	50.7	#10 of 14	Archive leaderboard	report
Unsupervised Image Classification	ImageNet	SCAN (ResNet-50)	ARI	27.5	#7 of 9	Archive leaderboard	report
Unsupervised Image Classification	ImageNet	SCAN (ResNet-50)	Accuracy (%)	39.9	#7 of 9	Archive leaderboard	report
Unsupervised Image Classification	STL-10	SCAN	Accuracy	80.90	#6 of 9	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: SCAN-clustering

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections