Search Results for author:
Found 4 papers, 1 papers with code
Trustworthy Partial Label Learning with Out-of-distribution Detection
PLL-OOD significantly enhances model adaptability and accuracy by merging self-supervised learning with partial label loss and pioneering the Partial-Energy (PE) score for OOD detection.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+2
FedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning
Extensive experiments on real-world medical data demonstrate the effectiveness of FedPIT in improving federated few-shot performance while preserving privacy and robustness against data heterogeneity.
FedNoisy: Federated Noisy Label Learning Benchmark
Federated learning has gained popularity for distributed learning without aggregating sensitive data from clients.
Graph based Label Enhancement for Multi-instance Multi-label learning
Multi-instance multi-label (MIML) learning is widely applicated in numerous domains, such as the image classification where one image contains multiple instances correlated with multiple logic labels simultaneously.
