Bayesian Principles Improve Prompt Learning In Vision-Language Models
- URL: http://arxiv.org/abs/2504.14123v1
- Date: Sat, 19 Apr 2025 00:48:09 GMT
- Title: Bayesian Principles Improve Prompt Learning In Vision-Language Models
- Authors: Mingyu Kim, Jongwoo Ko, Mijung Park,
- Abstract summary: We propose a new training objective function based on a Bayesian learning principle to balance adaptability and generalizability.<n>This objective establishes a balance by allowing the fine-tuned model to adapt to downstream tasks while remaining close to the pre-trained model.
- Score: 10.593234723172767
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Prompt learning is a popular fine-tuning method for vision-language models due to its efficiency. It requires a small number of additional learnable parameters while significantly enhancing performance on target tasks. However, most existing methods suffer from overfitting to fine-tuning data, yielding poor generalizability. To address this, we propose a new training objective function based on a Bayesian learning principle to balance adaptability and generalizability. We derive a prior over the logits, where the mean function is parameterized by the pre-trained model, while the posterior corresponds to the fine-tuned model. This objective establishes a balance by allowing the fine-tuned model to adapt to downstream tasks while remaining close to the pre-trained model.
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