PixCalibrate: Pixel-Mixing Guided Calibration And Robustness for Image Corruption

Reliable image classification requires models to remain accurate and calibrated under distribution shift. However, models trained on clean data are often prone to incorrect and overconfident predictions on corrupted or out-of-distribution test data. Existing augmentation frameworks partially address this issue, but they typically rely on global interpolation or introduce sharp region-level changes, neither of which simulates real-world corruptions. In this work, we propose PixCalibrate, a training-time pixel-level mixing approach for improving both robustness and calibration under corrupted settings. PixCalibrate generates augmented samples that preserve the semantic structure of an anchor image, while injecting a controlled fraction of pixels from a perturbation source—auxiliary image, producing fine-grained, spatially distributed perturbations that closely mimic real-world corruptions without disrupting the overall image structure. PixCalibrate consistently achieves strong accuracy and calibration across standard image corruption benchmarks and outperforms multiple prior augmentation frameworks.