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cloud.google.com·7h ago
AI Research
arXiv CS.AI·19h ago·5 min read
Calibration in One Go: Jointly Learning Accurate and Calibrated Neural Networks
TL;DR
A novel approach to calibrating neural networks allows for joint optimization of accuracy and calibration without needing a separate calibration dataset. This method simplifies the calibration process and outperforms existing state-of-the-art techniques.
✦ Why It Matters
Implement this joint calibration method to streamline your neural network training process today.
Key Takeaways
- Adopt the bilevel Stackelberg formulation for calibration.
- Use the final classification layer as a calibrator.
- Optimize accuracy and calibration simultaneously.
- Reduce overhead in multi-task learning scenarios.
How It Works
The proposed method integrates calibration directly into the training of neural networks by using a bilevel Stackelberg formulation. This allows the model to learn calibration parameters alongside its main parameters, optimizing both accuracy and calibration in a single training process.
By employing cross-entropy loss, the model can adjust its outputs effectively without needing a separate calibration dataset or additional calibration loss functions.