Parameter Efficient Fine Tuning (PEFT) generally involves:

Parameter Efficient Fine Tuning (PEFT) focuses on optimizing models with limited resources by adjusting only a small subset of parameters while keeping the majority of the network unchanged. This approach allows for efficient fine-tuning of pre-trained models to specific tasks, reducing computational costs and training time.

In this context, retaining a small number of neurons (or parameters) during the fine-tuning process enables the model to adapt to new data or tasks with minimal changes. By freezing the majority of the network, PEFT leverages the already learned representations and features, making the training process more efficient and less prone to overfitting on smaller datasets.

The other choices do not align with the principles of PEFT. For example, retraining all neurons would not be efficient and would negate the advantages of fine-tuning. Freezing all layers of the network would mean no learning occurs, which contradicts the idea of fine-tuning. Utilizing data augmentation techniques, while beneficial in some contexts, is not a defining characteristic of PEFT itself, but rather a general strategy to enhance training datasets. Therefore, the approach of retaining a limited number of neurons while keeping others frozen stands out as the essence of PEFT, making it the correct choice.