Hugging Face Trending Papers
PaSta: Noisy Node Classification with Partial Label Learning
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arXiv Machine Learning
Aug 27
PaSta: Noisy Node Classification with Partial Label Learning
PaSta introduces a Partial label-based Self‑training framework for noisy node classification on graphs. The method trains multiple annotators to generate high‑quality partial labels, then uses a partial‑label classification model with two loss functions to learn both labels and representations. A closed‑loop self‑training strategy further refines annotators, yielding an average 1.1% improvement over state‑of‑the‑art methods across five datasets.
Hugging Face Trending Papers
Aug 7
Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutions
Deep learning has achieved great success in recent years thanks to the availability of high-quality, well-annotated training data. However, this requirement is often not met in real-world applications.
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.
arXiv Machine Learning
Aug 10
Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutions
arXiv:2608. 06896v1 Announce Type: new Abstract: Deep learning has achieved great success in recent years thanks to the availability of high-quality, well-annotated training data.
arXiv AI
Jun 9
Deep Active Re-Labeling: Toward Noise-Resilient Annotation Efficiency
arXiv:2606. 08718v1 Announce Type: cross Abstract: While Deep Active Learning (DAL) effectively reduces human annotation costs, its efficacy is constrained by human annotation errors.
Hugging Face Trending Papers
Aug 24
FedCC: Towards Addressing Label Distribution Skews in Distillation-Based Federated Learning
Federated Learning (FL) enables distributed clients to collaboratively train models without sharing raw data, making it promising for leveraging massive devices in communication networks. In distillat...