The surgical fine-tuning strategy employed in MPGraf presents several potential limitations, particularly in its ability to generalize across diverse datasets and mitigate distribution shifts effectively.
Limited Generalization: While freezing certain layers during fine-tuning can help retain previously learned knowledge, it may also restrict the model's ability to adapt to new data distributions. If the frozen parameters are not well-aligned with the target dataset, the model may underperform. This limitation can be particularly pronounced in scenarios where the target dataset exhibits significant differences in feature distributions compared to the pre-training datasets.
Overfitting Risks: The surgical fine-tuning approach may lead to overfitting, especially when the target dataset is small or lacks diversity. The model might become too specialized in the nuances of the target data, losing its generalization capabilities.
Improvement Strategies: To enhance the surgical fine-tuning strategy, several approaches can be considered:
Dynamic Layer Freezing: Instead of a static freezing strategy, a dynamic approach could be implemented where layers are selectively unfrozen based on their performance on validation data. This would allow the model to adaptively adjust its parameters in response to the target dataset's characteristics.
Multi-Task Learning: Incorporating multi-task learning during the fine-tuning phase can help the model learn shared representations across related tasks, improving its robustness to distribution shifts. By training on auxiliary tasks that are relevant to the target domain, the model can develop a more generalized understanding.
Domain Adaptation Techniques: Implementing domain adaptation techniques, such as adversarial training or domain-invariant feature learning, can help the model better handle distribution shifts. These techniques can encourage the model to learn representations that are less sensitive to the specific characteristics of the training data.