Towards More Reliable Confidence Estimation.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Confidence estimation is crucial for trustworthy AI deployment.
• Existing methods struggle with label imbalance and out-of-distribution data.
• Deep models require reliable confidence metrics for safe application.

Purpose of the Study:

• To propose a meta-learning framework for improved confidence estimation.
• To enhance model trustworthiness by addressing key limitations.
• To generalize confidence estimation across diverse data distributions.

Main Methods:

• A meta-learning framework using virtual training/testing sets with distribution shifts.
• A virtual training and testing scheme to foster distributional generalization.
• Incorporation of a modified meta-optimization rule for flat meta-minima convergence.

Main Results:

• The framework simultaneously improves performance under label imbalance and with out-of-distribution data.
• Demonstrated effectiveness across monocular depth estimation, image classification, and semantic segmentation tasks.
• Achieved more reliable confidence estimation for deep learning models.

Conclusions:

• The proposed meta-learning framework offers a robust solution for confidence estimation.
• It enhances the trustworthiness and safety of deep models in real-world deployments.
• The method generalizes well to various tasks and data distribution challenges.