| Year | Title | Author | Publication | Code | Tags | Notes | Tasks | Datasets |
|---|---|---|---|---|---|---|---|---|
| 2021 | Active Multilabel Crowd Consensus | Yu et al. | IEEE TNNLS | - | Annotation, |
the selected samples in active crowdsourcing learning are annotated by different nonreliable workers, whose annotations might be incorrect. | ||
| 2021 | SEAL: Semisupervised Adversarial Active Learning on Attributed Graphs | Li et al. | IEEE TNNLS | - | graphs neural network, semi-supervised learning, adversarial learning |
Node Classification |
Citeseer, Cora, DBLP, Pubmed | |
| 2021 | Fast and Effective Active Clustering Ensemble Based on Density Peak | Shi et al. | IEEE TNNLS | - | Active Clustering, Hybrid |
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| 2021 | Efficient Active Learning by Querying Discriminative and Representative Samples and Fully Exploiting Unlabeled Data | Gu et al. | IEEE TNNLS | - | batch mode active learning, hybrid(informativeness+representativeness) |
codrna, ijcnn1, usps, mushrooms, a9a, svmguide1, isolet, phishing, letter, w3a |