简介：Fine-tuning and linear probing are two popular techniques used to improve the performance of pre-trained models. However, fine-tuning can distort pre-trained features, leading to poor performance on out-of-distribution data. In this article, we explore the differences between fine-tuning and linear probing, and provide an in-depth analysis of their strengths and weaknesses.
Fine-tuning and linear probing are two commonly used techniques to adapt pre-trained models to new tasks. While both techniques aim to improve model performance, they differ in their approach and characteristics. Fine-tuning, which involves updating the model’s parameters to fit a specific task, has been found to distort the pre-trained features, leading to poor performance on out-of-distribution data. On the other hand, linear probing, which involves initializing a new model and training it from scratch on the target task, preserves the pre-trained features and can perform better on unseen data.
The main difference between fine-tuning and linear probing lies in their approach to adapting pre-trained models. Fine-tuning modifies the pre-trained model’s parameters to fit the target task, effectively retraining certain layers of the model. This allows the model to adapt to new data and improve performance on similar tasks. However, when fine-tuning is applied to tasks that are significantly different from the pre-training task, it can lead to overfitting and poor performance on unseen data.
In contrast, linear probing initializes a new model for the target task and trains it from scratch, without modifying the pre-trained model’s parameters. This allows the pre-trained features to be preserved and transferred to the target task. By only training the new model on the target task, linear probing can adapt to new data without overfitting. However, if the target task is significantly different from the pre-training task, linear probing may not achieve as good performance as fine-tuning.
To address the limitations of fine-tuning, some recent studies have proposed combining fine-tuning and linear probing. These methods first use linear probing to initialize a new model and then fine-tune it on the target task. This approach aims to preserve the pre-trained features while allowing for adaptation to new data. However, further research is needed to investigate the effectiveness of these combined methods.
In conclusion, fine-tuning and linear probing each have their advantages and disadvantages. Fine-tuning allows for adaptation to new tasks but can distort pre-trained features, leading to poor performance on out-of-distribution data. Linear probing preserves the pre-trained features but may not achieve as good performance as fine-tuning on significantly different tasks. To address these limitations, combining fine-tuning and linear probing may be a promising direction for future research.
As a next step, it would be interesting to explore more advanced techniques that aim to strike a balance between fine-tuning and linear probing. One such approach could involve using a subset of pre-training data or regularization techniques to mitigate overfitting while preserving the benefits of fine-tuning.
It’s also worth noting that both fine-tuning and linear probing are influenced by the choice of pre-training task and dataset. Therefore, it’s essential to consider the similarity between the pre-training task and the target task when selecting an appropriate adaptation technique. For example, if the target task involves a similar domain or concept as the pre-training task, fine-tuning may be more suitable. However, if the target task involves a different domain or concept, linear probing or a combination of both techniques may be more appropriate.
Finally, it’s worth mentioning that both fine-tuning and linear probing require labeled target data for training. Labeled data is crucial for assessing model performance and for iterative improvement. Therefore, obtaining labeled data for target tasks can be a challenge and may require additional resources or crowd-sourcing efforts.
In summary, fine-tuning and linear probing are two popular techniques for adapting pre-trained models to new tasks. While fine-tuning allows for adaptation to new data, it can distort pre-trained features leading to poor performance on out-of-distribution data. Linear probing preserves the pre-trained features but may not achieve as good performance as fine-tuning on significantly different tasks. Future research should explore advanced techniques that strike a balance between fine-tuning and linear probing to improve transfer learning capabilities.