SCAN-clustering
Semantic Clustering by Adopting Nearest Neighbours
SCAN automatically groups images into semantically meaningful clusters when ground-truth annotations are absent. SCAN is a two-step approach where feature learning and clustering are decoupled. First, a self-supervised task is employed to obtain semantically meaningful features. Second, the obtained features are used as a prior in a learnable clustering  approach.

Image source: [Gansbeke et al.](https://arxiv.org/pdf/2005.12320v2.pdf)
SCAN: Learning to Classify Images without Labels
https://paperswithcode.com/paper/learning-to-classify-images-without-labels
SCAN: Learning to Classify Images without Labels