A data-free pretraining step that places prompts in flatter loss regions improves calibration and performance when used as initialization for test-time prompt tuning of vision-language models.
The generalized householder transforma- tion and sparse matrices.Linear Algebra and its Applica- tions, 90:221–234
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Improving Calibration in Test-Time Prompt Tuning for Vision-Language Models via Data-Free Flatness-Aware Prompt Pretraining
A data-free pretraining step that places prompts in flatter loss regions improves calibration and performance when used as initialization for test-time prompt tuning of vision-language models.