8 accepted papers
Graph contrastive learning (GCL) aims at narrowing positives while dispersing negatives, often causing a minority of samples with great similarities to gather as a small group. It results in two latent shortcomings in GCL: 1) **local cohesion** that a class cluster contains numerous independent smal…
Semi-supervised learning (SSL) is a classical machine learning paradigm dealing with labeled and unlabeled data. However, it often suffers performance degradation in real-world open-set scenarios, where unlabeled data contains outliers from novel categories that do not appear in labeled data. Existi…
Multi-label metric learning serve as an effective strategy to facilitate multi-label classification, aiming to learn better similarity metrics from multi-label examples. Existing multi-label metric learning approaches learn consistent metrics across all multi-label instances in the label space. Howe…
In multi-label learning, each training instance is associated with multiple labels simultaneously. Traditional multi-label learning studies primarily focus on closed set scenario, i.e. the class label set of test data is identical to those used in training phase. Nevertheless, in numerous real-world…
Partial label learning (PLL) aims to learn from inexact data annotations where each training example is associated with a coarse candidate label set. Due to its practicability, many PLL algorithms have been proposed in recent literature. Most prior PLL works attempt to identify the ground-truth labe…
In partial multi-label learning (PML), each training example is associated with a set of candidate labels, among which only some labels are valid. As a common strategy to tackle PML problem, disambiguation aims to recover the ground-truth labeling information from such inaccurate annotations. Howeve…
Label-specific features serve as an effective strategy to facilitate multi-label classification, which account for the distinct discriminative properties of each class label via tailoring its own features. Existing approaches implement this strategy in a quite straightforward way, i.e. finding the m…
Label-specific features serve as an effective strategy to learn from multi-label data with tailored features accounting for the distinct discriminative properties of each class label. Existing prototype-based label-specific feature transformation approaches work in a three-stage framework, where pro…