Bayesian Principles Improve Prompt Learning In Vision-Language Models

Abstract: Prompt learning is a popular fine-tuning method for vision-LLMs 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.

Evaluation of Bayesian Principles in Prompt Learning for Vision-Language Models

This paper explores the application of Bayesian principles to enhance prompt learning in Vision-Language Models (VLMs). Prompt learning is an efficient fine-tuning method, leveraging a small number of learnable parameters to improve performance on target tasks significantly. Despite its advantages, prompt learning often faces generalization challenges due to overfitting on fine-tuning datasets. This paper proposes a Bayesian learning framework to address these challenges, introducing a novel training objective that balances a model's adaptability and generalizability.

Proposed Methodology

The proposed approach incorporates a Bayesian framework with a specific focus on integrating Polya-Gamma (PG) augmented logistic regression. The core components are:

1. Objective Function with Bayesian Learning: The researchers crafted an objective function that construes a prior over the logits. The mean function of this prior is dictated by the pre-trained model, whereas the posterior distribution corresponds to the fine-tuned model. This structure allows fine-tuning that preserves the pre-trained model's global knowledge, enhancing generalization.

2. One-vs-Each Softmax Approximation: The methodology diverges from the standard softmax function, which can overfit to specific target labels. By employing a one-vs-each softmax approximation, the model becomes more resistant to overfitting, maintaining robustness even when constrained by very few training samples.

3. Polya-Gamma Augmentation: To refine the probabilistic approximation of the softmax distribution, the authors introduce Polya-Gamma augmentation. This technique aids in representing each sample's logits through a surrogate Gaussian distribution, effectively circumventing overfitting issues that traditional softmax methods may encounter.

Empirical Validation

The authors validate their Bayesian approach through extensive experiments involving several benchmark datasets. The primary evaluation metric is how well the models generalize to unseen classes after experiencing limited fine-tuning. Results indicate that the Bayesian framework consistently outperforms traditional methods, particularly in cross-dataset and unseen category generalization tasks. Notably, the OVE-PG (One-vs-Each Softmax with Polya-Gamma augmentation) model demonstrated significant performance improvements, achieving higher average accuracy across various dataset categories compared to existing approaches.

Implications and Future Directions

The results presented in this paper have profound implications for the field of machine learning, particularly in improving the generalization capabilities of fine-tuned models. By effectively integrating Bayesian principles with existing prompt learning strategies, this research paves the way for more robust and adaptable VLMs. It offers a practical framework that other researchers can build upon in scenarios requiring efficient and scalable adaptation to new data.

Furthermore, this approach's compatibility with existing models suggests its applicability in a diverse range of settings beyond vision-language tasks. Future research could explore further optimization of priors or consider the application of this framework to other fields where maintaining a balance between adaptability and generalizability is critical, such as natural language processing or robotics.

In conclusion, by integrating Bayesian principles into the foundation of prompt learning, this paper offers a compelling advancement in the quest to build models that better generalize across tasks and domains, aligning closely with contemporary needs in AI research and application.