Socrates Loss: Unifying Confidence Calibration and Classification by Leveraging the Unknown
Summary: arXiv:2604.12245v1 Announce Type: cross
Abstract
Deep neural networks, despite their high accuracy, often exhibit poor confidence calibration, limiting their reliability in high-stakes applications. Current ad-hoc confidence calibration methods attempt to fix this during training but face a fundamental trade-off: two-phase training methods achieve strong classification performance at the cost of training instability and poorer confidence calibration, while single-loss methods are stable but underperform in classification. This paper addresses and mitigates this stability-performance trade-off.
Introduction
The reliability of deep neural networks (DNNs) is crucial, especially in critical fields such as healthcare, autonomous driving, and finance. As they continue to achieve high accuracy rates, the challenge of confidence calibration remains paramount. This issue arises when a model’s predicted probabilities do not reflect the true likelihood of correctness. Consequently, the need for effective confidence calibration methods is more pressing than ever.
Proposed Solution: Socrates Loss
In addressing the calibration-performance trade-off, we introduce Socrates Loss, a novel, unified loss function that explicitly incorporates uncertainty through an auxiliary unknown class. This innovative approach enables the model to optimize both classification and confidence calibration concurrently.
Key Features of Socrates Loss
- Dynamic Uncertainty Penalty: The inclusion of a dynamic penalty for uncertainty directly influences the loss function, promoting more accurate predictions.
- Regularization Against Miscalibration: The theoretical framework ensures that the model remains well-calibrated while minimizing overfitting.
- Stability and Performance: Unlike traditional two-phase methods, Socrates Loss maintains training stability without sacrificing classification performance.
Experimental Results
We conducted extensive experiments across four benchmark datasets using various architectures to validate the effectiveness of Socrates Loss. The results indicate a consistent improvement in training stability and a favorable accuracy-calibration trade-off.
Findings
Our findings reveal that Socrates Loss often converges faster than existing methods, demonstrating its potential as a superior approach to confidence calibration. The incorporation of an unknown class serves as a robust mechanism to enhance both classification accuracy and model reliability.
Conclusion
The introduction of Socrates Loss represents a significant advancement in the field of deep learning. By elegantly merging the concepts of confidence calibration and classification, this approach promises to enhance the reliability of neural networks in critical applications. Future research will explore further refinements and applications of this loss function across various domains.
References
For more information, please refer to the full paper available on arXiv: arXiv:2604.12245v1.