Methods › General › Knowledge Distillation › SSKD
Semi-Supervised Knowledge Distillation
SSKD
Introduced by Lingxiao He et al. in Semi-Supervised Domain Generalizable Person Re-Identification
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Semi-Supervised Knowledge Distillation is a type of knowledge distillation for person re-identification that exploits weakly annotated data by assigning soft pseudo labels to YouTube-Human to improve models' generalization ability. SSKD first trains a student model (e.g. ResNet-50) and a teacher model (e.g. ResNet-101) using labeled data from multi-source domain datasets. Then, SSKD develops an auxiliary classifier to imitate the soft predictions of unlabeled data generated by the teacher model. Meanwhile, the student model is also supervised by hard labels and predicted soft labels by the teacher model for labeled data.
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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Semi-Supervised Domain Generalizable Person Re-Identification 11 Aug 2021 · 3 repositories · arXiv:2108.05045Syntology ran 6 of 7 samples · 1 unverified
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 |
|---|---|
| Generalizable Person Re-identification | 1 |
| Knowledge Distillation | 1 |
| Person Re-Identification | 1 |
| Transductive Learning | 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
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