Target-Agnostic Calibration under Distribution Shift with Frequency-Aware Gradient Rectification
arXiv:2508.19830v2 Announce Type: replace-cross 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 target-agnostic training framework for robust calibration. From a frequency perspective, FGR applies
The increasing deployment of AI models in critical real-world applications highlights the urgent need for robust calibration methods that do not rely on pre-existing knowledge of target domains.
Reliable confidence estimates are paramount for safety-critical AI deployments, and a target-agnostic approach significantly broadens the applicability and safety of neural networks under real-world distribution shifts.
This research introduces a training framework that enhances model calibration without needing prior target domain information, making AI systems more trustworthy and deployable in dynamic environments.
- · AI developers
- · Safety-critical AI sectors
- · Autonomous systems
- · Healthcare AI
- · Developers relying solely on post-hoc calibration
- · AI systems with poor out-of-distribution generalization
Increased trust and adoption of deep neural networks in applications where reliability under distribution shift is crucial.
Accelerated development of AI systems for dynamic, unpredictable real-world environments without extensive domain-specific data collection.
Potential for new regulatory frameworks for AI safety that incorporate requirements for robust calibration under unknown shifts.
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Read at arXiv cs.AI