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Analysing the Noise Model Error for Realistic Noisy Label Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Estimate the noise matrix using Fixed Sampling instead of Variable Sampling (ablation) | Noise-model estimation and the base model trained with noise handling · NoisyNER | expected squared error | Helped |
| Estimate the noise matrix using Fixed Sampling instead of Variable Sampling (ablation) | Base model trained with noise handling · NoisyNER | F1 | Helped |
| Add a noise model to handle noisy training labels (ablation) | Base model · NoisyNER and Clothing1M | test performance | Helped |
| Add a noise model to handle noisy training labels (ablation) | Base model with very few clean training samples · NoisyNER and Clothing1M | test performance | Mixed |
| Increase the number of clean label pairs used to estimate the noise matrix (ablation) | Noise-model estimation · Synthetic noise, Clothing1M, and NoisyNER | expected squared error | Helped |
| Use Variable Sampling rather than Fixed Sampling (ablation) | Noise-model estimation · NoisyNER | expected squared error | Hurt |
| Use Variable Sampling rather than Fixed Sampling (ablation) | Base model trained with noise handling · NoisyNER | test F1 | Hurt |
| Improve the estimated noise matrix by reducing its estimation error | Base model trained with noise handling · Clothing1M and NoisyNER | test performance | Helped |
| Use the theoretical expected error as a predictor of the noise-matrix estimation error | Noise-model estimation · Synthetic noise, Clothing1M, and NoisyNER | squared error | Helped |
Closest papers by meaning
- Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations (ICLR 2022 · similarity 0.91)
- Learning the Latent Causal Structure for Modeling Label Noise (NeurIPS 2024 · similarity 0.88)
- Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization (NeurIPS 2025 · similarity 0.88)
- Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning (AAAI 2024 · similarity 0.88)
- NoiseGPT: Label Noise Detection and Rectification through Probability Curvature (NeurIPS 2024 · similarity 0.87)
- Mitigating Label Noise through Data Ambiguation (AAAI 2024 · similarity 0.86)
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