Training-free graph SSL matches GCN with 5× fewer labels — live demo [P]
Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.
Hi all,
I have been working on this method based on a hunch along with many llm for quite some time. Though first it was being engineered by me but I was learning in supervised ml area but this hunch took to semi-supervised ml and that to too deep. I then became llm orchestrator of sort while 4 llm's tried to figure it out.
I put up a live demo on Hugging Face Spaces where you can try it yourself — set the number of labels, click run, see the accuracy. No installation, no code required. Brief about method
Optimus — Graph SSL under Extreme Label Scarcity
Key Results (PathMNIST, N=2000, 9 classes)
| Labels Total | Optimus | GCN |
|---|---|---|
| 9(1 per class) | 73.9 | 60.6 |
| 27(3 per class) | 77.3 | 68.5 |
| 45(5 per class) | 79.8 | 77.1 |
https://huggingface.co/spaces/Keshu007/optimus-graph-ssl
Edit : You can can even run the code on your own dataset
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