arXiv Machine Learning
Decoupled Optimization for Teacher-Student Semi-Supervised Learning via a Pioneer Student
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
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
Aug 11
Matching Supervision to the Student's Learning Capacity: A Unified Framework for On-Policy Self-Distillation
arXiv:2608. 08176v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) improves the reasoning abilities of LLMs by internalizing privileged context into model parameters through self-distillation.
arXiv Computer Vision
1d ago
Scalable Patch-Level Self-Supervised Learning
arXiv:2610.10013v1 Announce Type: new Abstract: Self-supervised learning (SSL) at scale produces powerful visual representations. However, most scalable SSL methods rely on ad hoc combinations of mul...
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
Sep 23
A Practical Recipe for Semi-Supervised Federated ASR: Online Pseudo-Labels with Server Update Stabilization
The paper presents a practical approach to semi‑supervised federated learning for automatic speech recognition (ASR). It demonstrates that using a per‑client online teacher combined with a stabilizing server‑side anchor—where the server continues training on labeled data between rounds—significantly reduces divergence caused by pseudo‑label errors. The authors provide design guidelines that improve in‑domain performance by an average of 20.8 % and cross‑domain performance by 10.0 % over the best prior method, narrowing the gap to fully‑supervised federated learning.
arXiv AI
Sep 1
Learning Dynamics of Logits Debiasing for Long-Tailed Semi-Supervised Learning
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...