← Back to all news
arXiv Machine Learning By Li Yuan, Yaxin Hou, Jiawei Tang, Yongbiao Gao, Yuheng Jia

Hub for Outliers, Spokes for Inliers: Uniform Latent Space Construction for Dual-Mismatched Semi-Supervised Learning

Read the original on arXiv Machine Learning →

The paper introduces a hub‑spoke latent space design for dual‑mismatched semi‑supervised learning, where known classes are uniformly arranged around a central hub and each class forms a compact cluster around its prototype. The hub serves as an anchor for a low‑evidence region that captures high‑uncertainty unknown class samples, thereby reducing majority‑class domination and improving feature discriminability. Experiments demonstrate that this approach outperforms state‑of‑the‑art methods, achieving up to a 3.25% improvement across various settings.

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.

Related stories

arXiv AI
Aug 25

FedCC: Towards Addressing Label Distribution Skews in Distillation-Based Federated Learning

FedCC is a new algorithm for distillation-based federated learning that tackles label distribution skew by allowing clients to mark ambiguous samples as 'unknown' instead of forcing a potentially wrong classification. By adding this extra class and calibrating pseudo-labels on a public dataset, FedCC balances confidence across majority and minority classes. Experiments show that FedCC outperforms existing methods, achieving 67.3% accuracy even when each client has data from only one of ten classes, whereas baselines drop to near-random performance.

By Wenxuan Ye, Onur Ayan, Xueli An, Georg Carle
efficiency
More like this →
arXiv Machine Learning
Sep 2

SAGE: Subpopulation-Aware Generative Enhancement for Mitigating Spurious Correlations

SAGE (Subpopulation-Aware Generative Enhancement) is a two-stage generative augmentation framework designed to mitigate spurious correlations in machine learning when group labels are unavailable. It uses cluster-derived sub-labels and class labels to fine‑tune a conditional generative model and text encoder, producing synthetic data that fills underrepresented regions and creates a balanced validation set for last‑layer reweighting. Experiments show SAGE improves worst‑group accuracy to 89.5%, 85.7%, and 79.1% on Waterbirds, CelebA, and MetaShift, outperforming existing group‑label‑free baselines by up to 7.7 percentage points.

By Yiming Luo, Rongqiang Zhao, Jie Liu
fine-tuning
More like this →