AINeutralarXiv – CS AI · 6h ago6/10
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Target-Agnostic Calibration under Distribution Shift with Frequency-Aware Gradient Rectification
Researchers propose Frequency-aware Gradient Rectification (FGR), a training framework that improves neural network calibration under distribution shifts without requiring access to target domains. The method uses low-pass filtering to reduce spurious patterns while maintaining in-distribution performance through geometric constraint projection.