arXiv:2407. 05370v3 Announce Type: replace Abstract: Semi-supervised learning (SSL) algorithms often struggle to perform well when trained on imbalanced data.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2607. 16363v1 Announce Type: cross Abstract: A large body of Semi-supervised Learning~(SSL) algorithms encounter the threshold $\tau$ to select pseudo-labels.
arXiv:2607. 00113v1 Announce Type: new Abstract: Background.
The paper introduces C-Score, a diagnostic framework for evaluating pseudo‑label‑based semi‑supervised learning (SSL) when unlabeled data may contain out‑of‑distribution (OOD) samples. C-Score assesses training behavior across prediction, feature representation, and optimization, using metrics such as PLE, CCI, Sem‑Drift, and Grad‑Align. Experiments on CIFAR‑10 and CIFAR‑100 with various OOD sources show that C‑Score detects hidden degradation that clean accuracy alone fails to reveal, highlighting the need for internal diagnostic signals in SSL robustness assessment.
arXiv:2608.30699v1 Announce Type: cross Abstract: Long-tailed distributions are prevalent in real-world semi-supervised learning (SSL), where pseudo-labels tend to favor majority classes, leading to...
arXiv:2512. 10244v2 Announce Type: replace-cross Abstract: Semi-supervised few-shot learning (SSFSL) resembles real-world applications such as auto-annotation, as it aims to learn a model from a few labeled and abundant unlabeled task-specific examples to annotate the unlabeled ones.
arXiv:2601. 11670v3 Announce Type: replace-cross Abstract: Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable under model overconfidence and class imbalance.