AINeutralarXiv – CS AI · 7h ago6/10
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Calibrating Uncertainty for Zero-Shot Adversarial CLIP
Researchers propose an adversarial fine-tuning method for CLIP that addresses a critical gap in zero-shot classification: while perturbations degrade accuracy, they also suppress uncertainty estimates, causing overconfidence. The approach reparameterizes CLIP outputs as Dirichlet distribution parameters to jointly optimize for robustness and calibrated uncertainty, achieving competitive results across benchmarks.