Researchers have introduced CORD (Calibrator-Output Repair for Top-1 Decision Preservation), a novel post-fit adapter designed to improve post-hoc calibration in machine learning models. CORD ensures that while confidence scores are adjusted, the original top-1 prediction remains unchanged. This method repairs the calibrated probability vector to preserve the initial prediction, thereby achieving a zero Top-1 Prediction Change Rate (TPCR) by construction. Experiments on datasets like CIFAR-10, CIFAR-100, and ImageNet-1K demonstrate that CORD not only preserves predictions but also reduces metrics such as ECE, NLL, and Brier compared to direct calibrated outputs. AI
IMPACT Improves reliability of model confidence scores without sacrificing prediction accuracy.
RANK_REASON The cluster describes a new research paper detailing a novel method for post-hoc calibration in machine learning.
Read on Hugging Face Daily Papers →
- arXiv
- Calibrator-Output Repair for Top-1 Decision Preservation
- CIFAR-10
- CIFAR-100
- CORD
- ECE
- Top-1 Prediction Change Rate
- Brier
- ImageNet-1K
- Top-1 Prediction Change Rate (TPCR)
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