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Found 4 papers, 1 papers with code
Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels
However, these CL methods fail to be directly adapted to multi-label image classification due to the difficulty in defining the positive and negative instances to contrast a given anchor image in multi-label scenario, let the label missing one alone, implying that borrowing a commonly-used way from contrastive multi-class learning to define them will incur a lot of false negative instances unfavorable for learning.
Learning from Positive and Unlabeled Data with Augmented Classes
In this paper, we propose an unbiased risk estimator for PU learning with Augmented Classes (PUAC) by utilizing unlabeled data from the augmented classes distribution, which can be easily collected in many real-world scenarios.
A Similarity-based Framework for Classification Task
Similarity-based method gives rise to a new class of methods for multi-label learning and also achieves promising performance.
Expand Globally, Shrink Locally: Discriminant Multi-label Learning with Missing Labels
In multi-label learning, the issue of missing labels brings a major challenge.
