Methods › General › Domain Adaptation › SRDC
Structurally Regularized Deep Clustering
SRDC
Introduced by Hui Tang et al. in Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Structurally Regularized Deep Clustering, or SRDC, is a deep network based discriminative clustering method for domain adaptation that minimizes the KL divergence between predictive label distribution of the network and an introduced auxiliary one. Replacing the auxiliary distribution with that formed by ground-truth labels of source data implements the structural source regularization via a simple strategy of joint network training.
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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Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering 19 Mar 2020 · 2 repositories · arXiv:2003.08607Syntology ran 2 of 6 samples · 4 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 |
|---|---|
| Clustering | 1 |
| Deep Clustering | 1 |
| Domain Adaptation | 1 |
| Unsupervised Domain Adaptation | 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