| CCMN: A General Framework for Learning With Class-Conditional Multi-Label Noise | 0 | 0.34 | 2023 |
| Active Learning for Open-set Annotation | 0 | 0.34 | 2022 |
| Partial Multi-Label Learning With Noisy Label Identification | 1 | 0.37 | 2022 |
| Improving deep label noise learning with dual active label correction | 0 | 0.34 | 2022 |
| Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training. | 0 | 0.34 | 2021 |
| Improving Model Robustness By Adaptively Correcting Perturbation Levels With Active Queries | 0 | 0.34 | 2021 |
| Dual Active Learning for Both Model and Data Selection. | 0 | 0.34 | 2021 |
| Label Distribution Learning with Label Correlations on Local Samples | 3 | 0.42 | 2021 |
| Recent Advances in Open Set Recognition: A Survey | 12 | 0.63 | 2021 |
| Multi-Label Learning with Pairwise Relevance Ordering. | 0 | 0.34 | 2021 |
| Asynchronous Active Learning with Distributed Label Querying. | 1 | 0.36 | 2021 |
| Crowdsourcing Aggregation With Deep Bayesian Learning | 0 | 0.34 | 2021 |
| Partial Multi-Label Learning with Meta Disambiguation | 0 | 0.34 | 2021 |
| Pu Active Learning For Recommender Systems | 0 | 0.34 | 2021 |
| Visual-guided attentive attributes embedding for zero-shot learning | 0 | 0.34 | 2021 |
| Active Learning With Query Generation For Cost-Effective Text Classification | 0 | 0.34 | 2020 |
| Uncertainty Aware Graph Gaussian Process For Semi-Supervised Learning | 0 | 0.34 | 2020 |
| Cost-effectively Identifying Causal Effect When Only Response Variable Observable | 0 | 0.34 | 2020 |
| Adaptive feature weighting for robust Lp-norm sparse representation with application to biometric image classification | 0 | 0.34 | 2020 |
| Partial Multi-Label Learning With Noisy Label Identification | 0 | 0.34 | 2020 |
| Semi-Supervised Partial Multi-label Learning | 1 | 0.35 | 2020 |
| LGSLRR: Towards Fusing Discriminative Ordinal Local and Global Structured Low-Rank Representation for Image Recognition | 0 | 0.34 | 2020 |
| Querying Representative and Informative Super-Pixels for Filament Segmentation in Bioimages | 0 | 0.34 | 2020 |
| Incremental Multi-Label Learning with Active Queries. | 3 | 0.38 | 2020 |
| Self-Paced Active Learning: Query the Right Thing at the Right Time | 0 | 0.34 | 2019 |
| ALiPy: Active Learning in Python. | 0 | 0.34 | 2019 |
| Towards Identifying Causal Relation Between Instances and Labels. | 0 | 0.34 | 2019 |
| Multi-View Active Learning for Video Recommendation. | 0 | 0.34 | 2019 |
| Active Sampling for Open-Set Classification without Initial Annotation | 0 | 0.34 | 2019 |
| Learning Class-Conditional GANs with Active Sampling | 1 | 0.35 | 2019 |
| Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part II. | 0 | 0.34 | 2019 |
| Dual Set Multi-Label Learning. | 0 | 0.34 | 2018 |
| Joint Estimation of Multiple Conditional Gaussian Graphical Models. | 2 | 0.40 | 2018 |
| Partial Multi-Label Learning. | 0 | 0.34 | 2018 |
| Cost-Effective Training of Deep CNNs with Active Model Adaptation. | 4 | 0.43 | 2018 |
| Cross modal similarity learning with active queries. | 2 | 0.36 | 2018 |
| WoCE: a framework for clustering ensemble by exploiting the wisdom of Crowds theory. | 3 | 0.36 | 2018 |
| Cost-Effective Active Learning for Hierarchical Multi-Label Classification. | 4 | 0.37 | 2018 |
| Active Feature Acquisition with Supervised Matrix Completion. | 2 | 0.39 | 2018 |
| Multi-instance multi-label active learning. | 2 | 0.37 | 2017 |
| Cost-Effective Active Learning from Diverse Labelers. | 2 | 0.36 | 2017 |
| Multi-label active learning by model guided distribution matching. | 11 | 0.48 | 2016 |
| Transfer Learning with Active Queries from Source Domain. | 5 | 0.43 | 2016 |
| Multi-Label Active Learning: Query Type Matters. | 12 | 0.54 | 2015 |
| Genome-Wide Protein Function Prediction through Multi-Instance Multi-Label Learning | 26 | 0.70 | 2014 |
| Active Query Driven By Uncertainty And Diversity For Incremental Multi-Label Learning | 19 | 0.77 | 2013 |
| Fast Multi-Instance Multi-Label Learning. | 12 | 0.57 | 2013 |
| Multi-instance multi-label learning | 153 | 4.00 | 2012 |
| Multi-Label Learning by Exploiting Label Correlations Locally. | 80 | 1.78 | 2012 |
| Multi-label hypothesis reuse | 15 | 0.61 | 2012 |
