Clustering
Semantic Clustering by Adopting Nearest Neighbours
Introduced by Gansbeke et al. in SCAN: Learning to Classify Images without LabelsSCAN 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.
Source: SCAN: Learning to Classify Images without Labels
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Computed Tomography (CT) | 1 | 8.33% |
| COVID-19 Diagnosis | 1 | 8.33% |
| Decision Making | 1 | 8.33% |
| Graph Representation Learning | 1 | 8.33% |
| De-identification | 1 | 8.33% |
| Classification | 1 | 8.33% |
| Clustering | 1 | 8.33% |
| General Classification | 1 | 8.33% |
| Image Classification | 1 | 8.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
