Train Time Calibration

Train-time calibration aims to improve the reliability of deep learning models by aligning their predicted confidence scores with the actual accuracy of their predictions. Current research focuses on developing novel loss functions and training techniques that directly address calibration during model training, rather than relying on post-hoc adjustments, exploring their effectiveness across various architectures including Convolutional Neural Networks (CNNs) and Transformers, and for tasks such as image classification and object detection. This work is crucial for enhancing the trustworthiness of AI systems in high-stakes applications where accurate confidence estimates are essential, such as medical diagnosis and autonomous driving.