Discover and Align Taxonomic Context Priors for Open-world Semi-Supervised Learning

Yu Wang, Zhun Zhong, Pengchong Qiao, Xuxin Cheng, Xiawu Zheng +4 more
2/12/2026

No official code found. Best implementation: rain305f/TIDA (17 stars, last commit 2024-04-15).

Semantic Scholar

Code Implementations(1)

rain305f/TIDACommunity66%

[NeurIPS 2023] Discover and Align Taxonomic Context Priors for Open-world Semi-Supervised Learning

170Shell, PythonOct 7, 20232 years ago

Cite this paper

@article{wang2026discover,
  title  = {Discover and Align Taxonomic Context Priors for Open-world Semi-Supervised Learning},
  author = {Yu Wang and Zhun Zhong and Pengchong Qiao and Xuxin Cheng and Xiawu Zheng and Chang Liu and N. Sebe and Rongrong Ji and Jie Chen},
  year   = {2026},
  url    = {https://api.semanticscholar.org/CorpusID:b21e1b0e4ce8c8d73f2360950363230b80ab143b},
  journal = {NEURIPS 2023 2023}
}

Discussion