A proposed unified regularizer for CLIP prompt learning, combining Fisher information and confidence penalties, is reported to improve few-shot accuracy and calibration, but the core combined method is not tested and key definitions conflict.
Causal covari- ate shift correction using fisher information penalty
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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models
A proposed unified regularizer for CLIP prompt learning, combining Fisher information and confidence penalties, is reported to improve few-shot accuracy and calibration, but the core combined method is not tested and key definitions conflict.