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awesome-partial-label-learning
A collection of papers and related materials dedicated to partial label learning.
https://github.com/wwangwitsel/awesome-partial-label-learning
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Surveys
- A brief introduction to weakly supervised learning - This article is a classic survey on weakly-supervised learning.
- Disambiguation-free partial label learning - This article is an up-to-date survey on partial label learning.
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Traditional PLL problems
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Identification-based methods
- Learning with multiple labels - A maximum likelihood approach with expectation maximization (PL-EM).
- A conditional multinomial mixture model for superset label learning - Logistic StickBreaking Conditional Multinomial Model (LSB-CMM).
- Maximum margin partial label learning - M3PL which maximize the margin between the maximum output of candidate labels and the maximum output of any other labels.
- Maximum margin partial label learning - A extended version of M3PL in ACML'15.
- Leveraging latent label distributions for partial Label Learning - partial label learning with LAtent Label distributiOns (LALO) which regresses the label confidence matrix with local consistency.
- Partial label learning with self-guided retraining - Self-gUided REtraining (SURE) which regresses the label confidence matrix with infinite norm of label confidence matrix.
- Partial Label Learning by Semantic Difference Maximization - Semantic DIfference Maximization (SDIM) which regresses the label confidence matrix with semantic maximization.
- Classification with partial labels - PL-SVM which maximize the margin between the maximum output of candidate labels and maximum output of non-candidate labels.
- A conditional multinomial mixture model for superset label learning - Logistic StickBreaking Conditional Multinomial Model (LSB-CMM).
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Averaging-based methods
- Learning from ambiguously labeled examples - KNN.
- Learning from ambiguously labeled images
- Learning from partial labels - Convex Learning from Partial Labels (CLPL) which distinguish the average output of candidate labels from that of non-candidate labels.
- A regularization approach for instance-based superset label learning
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Other methods
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Categories