Research map / Label-efficient and robust learning / Recognition and long-tail
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add data ensembling and composition | Isotonic Regression · ImageNet | data efficiency | Helped |
| Add model ensembling | Temperature Scaling · ImageNet | expressive power | Helped |
| Calibrate with Isotonic Regression | 50-layer Wide ResNet · ImageNet | accuracy | Hurt |
Closest papers by meaning
- Beyond One-Hot Labels: Semantic Mixing for Model Calibration (ICML 2025 · similarity 0.88)
- Reassessing How to Compare and Improve the Calibration of Machine Learning Models (ICLR 2025 · similarity 0.87)
- On Temperature Scaling and Conformal Prediction of Deep Classifiers (ICML 2025 · similarity 0.86)
- Tailoring Mixup to Data for Calibration (ICLR 2025 · similarity 0.86)
- Improving Accuracy and Calibration via Differentiated Deep Mutual Learning (CVPR 2025 · similarity 0.86)
- On the Limitations of Temperature Scaling for Distributions with Overlaps (ICLR 2024 · similarity 0.86)
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