A CLIP fine-tuning method that penalizes output differences from the pretrained model and prediction differences under augmentation improves both ID accuracy and OOD robustness in reported experiments.
Language models are few-shot learners
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Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance
A CLIP fine-tuning method that penalizes output differences from the pretrained model and prediction differences under augmentation improves both ID accuracy and OOD robustness in reported experiments.