Title: SECA: Self-Guided Model Calibration
Primary area: optimization
Keywords: Loss function, Deep Neural Networks, Model Calibration

Abstract: Deep learning models frequently exhibit poor calibration, where predicted confidence scores fail to align with actual accuracy rates, undermining model reliability in safety-critical applications. We propose a novel train-time calibration method named **SECA** (**Se**lf-guided Model **Ca**libration), a **hyper-parameter-free** approach designed to improve predictive calibration through dynamic confidence regularisation. SECA constructs adaptive soft targets by fusing batch-averaged model predictions with one-hot ground-truth labels during training, thereby creating a self-adaptive calibration mechanism that adapts target distributions based on the model's predictive behaviour. This leads to well-calibrated predictions without additional hyper-parameter tuning or significant computational overhead. Our theoretical analysis elucidates SECA's underlying mechanisms from entropy regularisation, gradient dynamics, and knowledge distillation perspectives. Extensive empirical evaluation demonstrates that SECA consistently achieves superior calibration performance compared to the Cross-Entropy loss and other state-of-the-art calibration methods across diverse architectures (CNN, ViT, BERT) and benchmark datasets in visual recognition and natural language understanding.