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Partial-Multi-Label-Learning

A curated list of resources for Partial-Multi-Label-Learning

Papers & Code

  • 2018-AAAI - Partial Multi-Label Learning. [Paper]
  • 2018-ICDM - Feature-induced Partial Multi-label Learning. [Paper]
  • 2019-IJCAI - Discriminative and Correlative Partial Multi-Label Learning [Paper]
  • 2019-AAAI - Partial Multi-Label Learning by Low-Rank and Sparse Decomposition. [Paper]
  • 2019-ICDM - Discriminatively relabel for partial multi-label learning. [Paper]
  • 2020-AAAI - Partial Multi-Label Learning with Label Distribution. [Paper] [Code]
  • 2020-KDD - Partial Multi-Label Learning via Probabilistic Graph Matching Mechanism. [Paper]
  • 2020-IJCAI - Partial Multi-Label Learning via Multi-Subspace Representation. [Paper] [Code]
  • 2020-AAAI - Multi-View Partial Multi-label Learning with Graph-based Disambiguation. [Paper] [Code]
  • 2020-ICDM - Partial Multi-label Learning using Label Compression. [Paper]
  • 2020-IJCAI - Recovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning. [Paper]
  • 2020-ICDM - Semi-Supervised Partial Multi-Label Learning. [Paper]
  • 2020-DASFAA - Partial Multi-label Learning with Label and Feature Collaboration. [Paper]
  • 2021-AAAI - Adversarial Partial Multi-Label Learning with Label Disambiguation. [Paper]
  • 2021-TPAMI - Partial Multi-Label Learning via Credible Label Elicitation. [Paper] [Code]
  • 2021-TNNLS - Progressive Enhancement of Label Distributions for Partial Multilabel Learning. [Paper]
  • 2021-NIPS - Understanding Partial Multi-label Learning via Mutual Information. [Paper]
  • 2021-PRL - Semi-supervised partial multi-label classification with low-rank and manifold constraints. [Paper]
  • 2021-Information Sciences - Noisy label tolerance: A new perspective of Partial Multi-Label Learning. [Paper]
  • 2021-KIS - Partial multi-label learning with noisy side information. [Paper]
  • 2021-KBS - Partial multi-label learning with mutual teaching. [Paper]
  • 2021-KDD - Partial Multi-Label Learning with Meta Disambiguation. [Paper]
  • 2021-IJCAI - Partial Multi-Label Optimal Margin Distribution Machine. [Paper]
  • 2022-KBS - Partial multi-label learning via specific label disambiguation. [Paper]
  • 2022-PAAP - Graph-based Multi-view Partial Multi-label Learning. [Paper]
  • 2022-KBS - Partial multi-label learning based on sparse asymmetric label correlations. [Paper]
  • 2022-IJCNN - Partial Multi-Label Feature Selection. [Paper]
  • 2022-TMM - Global-Local Label Correlation for Partial Multi-Label Learning. [Paper]
  • 2022-TPAMI - Partial Multi-Label Learning With Noisy Label Identification. [Paper] [Code]
  • 2022-TPAMI - CCMN: A General Framework for Learning With Class-Conditional Multi-Label Noise. [Paper]
  • 2022-PR - Semi-supervised partial multi-label classification via consistency learning. [Paper]
  • 2022-AAAI - Partial Multi-Label Learning via Large Margin Nearest Neighbour Embeddings. [Paper]
  • 2022-Arxiv - A Deep Model for Partial Multi-label Image Classification with Curriculum Based Disambiguation. [Paper] [Code]
  • 2023-IJCNN - Landmark-based Partial Multi-label Learning with Noise Processing. [Paper]
  • 2023-TNNLS - Learning Accurate Label-Specific Features From Partially Multilabeled Data. [Paper]
  • 2023-Applied Intelligence - Learning with partial multi-labeled data by leveraging low-rank constraint and decomposition. [Paper]
  • 2023-TPAMI - Towards Enabling Binary Decomposition for Partial Multi-Label Learning. [Paper] [Code]
  • 2023-TMM - Dual Noise Elimination And Dynamic Label Correlation Guided Partial Multi-label Learning. [Paper]
  • 2023-TKDE - Multi-View Partial Multi-Label Learning via Graph-Fusion-Based Label Enhancement. [Paper]
  • 2023-DSAA - ProPML: Probability Partial Multi-label Learning. [Paper]
  • 2023-TCYB - Prior Knowledge Regularized Self-Representation Model for Partial Multilabel Learning. [Paper]
  • 2023-NIPS - Partial Multi-Label Learning with Probabilistic Graphical Disambiguation. [Paper] [Code]
  • 2023-Applied Intelligence - Learning with partial multi-labeled data by leveraging low-rank constraint and decomposition. [Paper]
  • 2023-Information Sciences - Partial multi-label feature selection via subspace optimization. [Paper]
  • 2023-ICME - Partial multi-label learning: exploration of binary ground-truth labels. [Paper]
  • 2023-KBS - Partial multi-label learning via three-way decision-based tri-training. [Paper]
  • 2023-Arxiv - Learning Reliable Representations for Incomplete Multi-View Partial Multi-Label Classification. [Paper]
  • 2023-IJCAI - Deep Partial Multi-Label Learning with Graph Disambiguation. [Paper]
  • 2023-TFS - Partial Multilabel Learning Using Fuzzy Neighborhood-Based Ball Clustering and Kernel Extreme Learning Machine. [Paper]
  • 2024-KBS - A two-stage multi-view partial multi-label learning for enhanced disambiguation. [Paper]
  • 2024-KBS - Partial multi-label learning via semi-supervised subspace collaboration. [Paper]
  • 2024-KBS - Partial multi-label learning via robust feature selection and relevance fusion optimization. [Paper]
  • 2024-Information Sciences - Learning shared and non-redundant label-specific features for partial multi-label classification. [Paper]
  • 2024-Information Sciences - PML-ED: A method of partial multi-label learning by using encoder-decoder framework and exploring label correlation. [Paper]
  • 2024-TNNLS - Partial Multilabel Learning Using Noise-Tolerant Broad Learning System With Label Enhancement and Dimensionality Reduction. [Paper] [Code]
  • 2024-KIS - Few-shot partial multi-label learning with synthetic features network. [Paper]
  • 2024-TMM - Negative Label and Noise Information Guided Disambiguation for Partial Multi-Label Learning. [Paper] [Code]