arXiv Machine Learning
Jul 2
SemiScope: Disentangling Classifier Tuning and Joint Optimization in Semi-Supervised Security Classification
arXiv:2607. 00113v1 Announce Type: new Abstract: Background.
C-Score: Beyond Accuracy for Robustness Assessment in Semi-Supervised Learning under Open-World Unlabeled Contamination
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.
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arXiv Machine Learning
Jun 16
Imbalanced Semi-Supervised Learning via Label Refinement and Threshold Adjustment
arXiv:2407. 05370v3 Announce Type: replace Abstract: Semi-supervised learning (SSL) algorithms often struggle to perform well when trained on imbalanced data.
arXiv Machine Learning
Jul 21
AOE: Exhaustive Out-of-Distribution Detection via Recalibrating Outlier Labels
arXiv:2605. 28021v2 Announce Type: replace Abstract: Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world and safety-critical scenarios, where test inputs may deviate from the training distribution and overconfident predictions on unknown samples can lead to unreliable decisions.
arXiv Machine Learning
Jun 24
Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective
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 AI
Sep 15
Follow the Geometry, Not the Model: Cold Start Semi-Supervised Learning
arXiv:2609.14451v1 Announce Type: cross Abstract: Modern semi-supervised learning (SSL) couples pseudo-label generation and classifier training, using the classifier's own confidence to select the ps...
arXiv Machine Learning
Jul 21
MTSSL: Meta-Thresholding Semi-Supervised 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 AI
Jun 9
SafeECGMatch: Calibration-Aware Joint Frequency and Time Space Semi-Supervised Learning for Open-Set ECG Classification
arXiv:2606. 08037v1 Announce Type: cross Abstract: Electrocardiogram (ECG) classification models often suffer from severe label scarcity, making semi-supervised learning (SSL) an attractive strategy for reducing annotation costs.
arXiv Machine Learning
6d ago
Classifier-Dependent Benefits of Pseudo-Labeling for Semi-Supervised Android Malware Attribution
The study evaluates pseudo‑labeling for semi‑supervised learning on Android malware attribution using six classifiers. Results show that the benefit of SSL varies strongly by classifier: SVM gains the most, LightGBM improves modestly, and Random Forest can be harmed at low label ratios. The approach particularly helps hard‑to‑classify families and achieves near‑optimal performance with about 800 labeled samples.
arXiv Machine Learning
Aug 26
When Does Self-Supervised Pretraining Help Tabular Models? A Study of Label Scarcity and Missing Data
arXiv:2608.24381v1 Announce Type: new Abstract: Self-supervised learning (SSL) has emerged as a promising approach for tabular data, yet its efficacy under extreme label scarcity and test-time missin...
arXiv Machine Learning
Jun 3
Self-Soupervision: Cooking Model Soups without Labels
arXiv:2602. 02890v2 Announce Type: replace Abstract: Model soups are strange and strangely effective combinations of parameters.
arXiv Machine Learning
Jun 2
Generate in Reconstruction Space, Match in Semantic Space: Transport Geometry for One-Step Generation
arXiv:2606. 00514v1 Announce Type: new Abstract: Generative modeling and self-supervised representation learning (SSL) optimize structurally different objectives: generative training rewards distributional fidelity, while SSL rewards semantic coherence.
Hugging Face Trending Papers
Jul 14
Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training?
Self-supervision is a powerful technique for learning visual representations from unlabeled data. Existing techniques primarily adopt a two-stage approach for self-supervised learning (SSL): a pretraining stage on unlabeled data followed by a finetuning stage on labeled data.