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Related Experiment Videos

GAFD-CC: Global-aware feature decoupling with confidence calibration for out-of-distribution detection.

Kun Zou1, Yongheng Xu1, Jianxing Yu2

  • 1School of Computer and Engineering, Sun Yat-sen University, Guangzhou, 510000, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces Global-Aware Feature Decoupling with Confidence Calibration (GAFD-CC) for improved out-of-distribution (OOD) detection in machine learning models. GAFD-CC enhances model reliability by refining decision boundaries and boosting discriminative performance.

Keywords:
Confidence calibrationFeature decouplingOut-of-distribution detection

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Vision

Background:

  • Out-of-distribution (OOD) detection is crucial for reliable AI systems.
  • Current post-hoc methods often ignore feature-logit correlations, limiting OOD detection effectiveness.

Purpose of the Study:

  • To propose a novel method, Global-Aware Feature Decoupling with Confidence Calibration (GAFD-CC), for enhanced OOD detection.
  • To refine decision boundaries and improve the discriminative performance of learning models.

Main Methods:

  • Global-aware feature decoupling guided by classification weights to align features.
  • Extraction of positively and negatively correlated features for boundary refinement and false positive suppression.
  • Adaptive fusion of decoupled features with multi-scale logit-based confidence.

Main Results:

  • GAFD-CC demonstrates competitive performance on large-scale benchmarks.
  • The method shows strong generalization ability compared to state-of-the-art techniques.
  • GAFD-CC effectively refines decision boundaries and enhances OOD detection.

Conclusions:

  • GAFD-CC offers a robust approach to OOD detection by leveraging feature-logit correlations.
  • The proposed method improves the reliability and robustness of machine learning models in real-world scenarios.
  • GAFD-CC advances the field of OOD detection through its innovative feature decoupling and confidence calibration techniques.