Methods › Graphs › Graph Models › Hi-LANDER
Hi-LANDER
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Hi-LANDER is a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an unknown number of identities using an image annotated with labels belonging to a disjoint set of identities. The hierarchical GNN uses an approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level. Unlike fully unsupervised hierarchical clustering, the choice of grouping and complexity criteria stems naturally from supervision in the training set.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Learning Hierarchical Graph Neural Networks for Image Clustering 3 Jul 2021 · 3 repositories · arXiv:2107.01319
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Clustering | 1 |
| Face Clustering | 1 |
| Graph Neural Network | 1 |
| Image Clustering | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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