A Closer Look to Positive-Unlabeled Learning from Fine-grained Perspectives: An Empirical Study

Confidence-aware pseudo-label selection and verifier training for semi-supervised LLM reasoning with minimal labels

An adaptive threshold selection policy that chooses thresholds on validation data using pseudo-label precision and sample count is introduced and is combined with confidence-aware verifier training to support confidence-based selection of pseudo-labeled subsets.

MD2PLL: a meta-learning framework for robust disambiguation in partial-label visual recognition

PseudoMapLabeler: Confidence-Aware Pseudo-Label Generation for Semi-Supervised Online Mapping

A critical challenge in deploying online HD map construction systems to real-world scenarios is the scarcity of labeled training data, which limits model generalization in diverse environments. To address this limitation, we propose a teacher-student semi-supervised learning (SSL) framework that generates high-quality pseudo-labels from unlabeled data through confidence-aware map refinement. Our approach first trains a teacher model on limited labeled data, then leverages Beta-distribution-based confidence maps to assess the reliability of predicted map elements across temporal observations. Unlike conventional filtering methods that discard entire elements, we introduce a spatial clipping technique that selectively preserves high-confidence regions while removing unreliable segments. The refined map elements serve as map priors that improve the teacher model's prediction accuracy on unlabeled data in a second pass. These enhanced predictions become pseudo-labels for training a student model from scratch, followed by fine-tuning on the original labeled data. Experimental results on the nuScenes dataset demonstrate that our teacher-student framework with refined pseudo-labels improves performance by +6.1 mAP under a low-label regime compared to training on labeled data alone, offering a practical solution to the labeled data scarcity problem in online HD map construction.

C-Score: Beyond Accuracy for Robustness Assessment in Semi-Supervised Learning under Open-World Unlabeled Contamination

C-Score, a compact framework that evaluates training behavior in three complementary spaces: prediction, feature representation, and optimization, suggests that clean accuracy alone is insufficient for evaluating SSL robustness in open-world environments, and that internal diagnostic signals are necessary for more reliable robustness assessment under unlabeled contamination.

Weakly-supervised Learning with Partial Multi-Labels by Leveraging Dual Label Correlation Perspectives

Multi-Label Learning (MLL) refers to inducing multi-label prediction models from the precisely labeled training dataset. However, in many real-world scenarios, e.g., crowdsourcing annotations, the training datasets are often only partially valid, where each training instance is associated with a candidate label set, covering ground-truth labels but also with irrelevant ones. Naturally, learning with such datasets, formally referred to as Partial Multi-label Learning (PML), involves many noisy supervised signals, hence imposing a significant challenge to the prediction model induction. To meet this challenge, we purify the noisy supervised signals by formulating the latent label distribution, i.e., the probability of a candidate label being a ground-truth one, and then jointly learn it with the prediction model by minimizing their regularized Wasserstein distance, i.e., a robust distance for distributions as well as involving label correlations. Therefore, we propose a novel PML method, namely Wasserstein Partial Multi-Label Learning with dual Label Correlation Perspectives (Wpml3cp), solved by the gradient descent with an augmented Lagrange multiplier technique. To further enhance the robustness of Wpml3cp against exceptionally high ratios of irrelevant labels, we extend it with a Dual-branch Competitive Cleansing mechanism, leading to Wpml3cp-D. Besides, we also analyze the generalization error bound and time complexity of Wpml3cp and Wpml3cp-D. The extensive experiments are constructed by comparing Wpml3cp and Wpml3cp-D with existing PML baselines across synthetic and real-world datasets, and empirical results demonstrate that Wpml3cp and Wpml3cp-D can outperform the PML baselines in various noisy levels.

Toward Robust Weakly Supervised Text Classification: Weak Supervision Generation and Correlation-Aware Supervision Propagation.

Multilabel text classification (MLTC) methods require enormous labeled training samples to ensure the model's performance, which involves significant manual labor costs. An alternative to conducting MLTC is to only employ predefined representative words of classes, namely category words, as the weak supervision. In this article, we propose a novel weakly supervised (WS) learning MLTC framework consisting of two parts. First, we propose a novel category word selection method, namely category word selection with significance ranking and crowd-sourcing (Cws-src), which generates confident category words by manually selecting from the topically reranked words using a new TW-ITF weighting scheme, thereby effectively mitigating the noises in pseudolabels by filtering repetitive and less significant terms for each class, leading to improved classification performance. Subsequently, we propose a generic WS learning MLTC method, namely WS multilabel text classification with correlation-aware label propagation (Wmltc-clp), which estimates accurate pseudolabels by propagating them over a text correlation graph. To evaluate the proposed framework, we conduct extensive experiments on nine benchmark datasets, including five sentiment analysis datasets and four prevalent MLTC datasets. The results demonstrate that Cws-src can generate more confident category words and Wmltc-clp can achieve significant improvements over the WS learning baselines. The maximum performance gains of Wmltc-clp over the best WS learning baseline methods reach 0.096, 0.081, 0.075, and 0.02 on Micro- $F1$ , Macro- $F1$ , average precision (AP), and ranking loss (RL) across all benchmark datasets.