Target-Agnostic Calibration under Distribution Shift

Target-Agnostic Calibration under Distribution Shift with Frequency-Aware Gradient Rectification

arXiv:2508.19830v2 Announce Type: replace Abstract: Real-world model deployments inevitably encounter distribution shifts, rendering the confidence estimates of deep neural networks highly unreliable, posing severe risks in safety-critical applications. Existing methods improve calibration via training-time regularization or post-hoc adjustment, but often rely on access to (or simulation of) target domains, limiting practicality. We propose Frequency-aware Gradient Rectification (FGR), a...