Research map / Label-efficient and robust learning / Noisy labels
Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add clean set to the objective (SPRL vs SPRL-W) (ablation) | SPRL · BlogFeedback, average over corruption ratios | error: 3.2766 -> 1.0426 | Helped |
| add self-paced scheme over weakly supervised baseline (SPRL vs WSL) (ablation) | SPRL · BlogFeedback, average | error: 1.6704 -> 1.0426 | Helped |
| use SPRL instead of clean-only LR-CL | SPRL · BlogFeedback, average | error: 1.162 -> 1.0426 | Helped |
| add clean set term at 40% corruption (SPRL vs SPRL-W) (ablation) | SPRL · Synthetic classification, 200 features, clean 100 | F1: 0.694 -> 0.751 | Helped |
| add clean-set term at 10% corruption (SPRL vs SPRL-W) (ablation) | SPRL · Synthetic classification, 200 features, clean 100 | F1: 0.944 -> 0.968 | No clear effect |
| SPRL vs SPL at 10% corruption on BlogFeedback | SPRL · BlogFeedback | error: 0.973 -> 0.971 | No clear effect |
| SPRL vs SVM-AL at 40% corruption | SPRL · Synthetic classification, 200 features, clean 100 | F1: 0.750 -> 0.751 | No clear effect |
Closest papers by meaning
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- Learning from Noisy Labels via Conditional Distributionally Robust Optimization (NeurIPS 2024 · similarity 0.85)
- Robust Logit Adjustment for Learning with Long-Tailed Noisy Data (AAAI 2025 · similarity 0.84)
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