PEFT parameter optimization technology: exploration to improve fine-tuning efficiency

PEFT (Parameter Efficient Fine-tuning) is a parameter efficient technology that optimizes the fine-tuning process of deep learning models, aiming to achieve efficient fine-tuning under limited computing resources. The researchers improved fine-tuning efficiency while maintaining model performance by employing a series of strategies to reduce the computational resources required for fine-tuning. These strategies include reducing the number of iterations of fine-tuning training, reducing the sampling rate of training data, and reducing the frequency of updating model parameters. Through these methods, PEFT can effectively fine-tune deep learning models under resource constraints, providing an effective solution for saving computing resources in practical applications.

PEFT has a wide range of applications, including image classification and natural language processing. The following examples illustrate the application of PEFT in detail.

1. Image classification

In the image classification task, PEFT can reduce the use of computing resources through the following strategies:

• Layer-by-layer fine-tuning: First, pre-train the model on a larger data set, and then fine-tune the model layer by layer. This approach can reduce the computational resources required for fine-tuning because there are fewer fine-tuning times per layer.
• Fine-tuning the head: Use the head of the pre-trained model (i.e. the fully connected layer) as the starting point for the new task and fine-tune it. This approach is often more efficient than fine-tuning the entire model because the head often contains task-relevant information.
• Data augmentation: Use data augmentation techniques to augment the training data set, thereby reducing the amount of data required for fine-tuning.

2. Target detection

In the target detection task, PEFT can reduce the use of computing resources through the following strategies:

• Fine-tune the backbone network: Use the backbone network of the pre-trained model as the starting point for a new task and fine-tune it. This approach can reduce the computational resources required for fine-tuning because the backbone network usually contains a general-purpose feature extractor.
• Incremental fine-tuning: Use the detection head of the pre-trained model as the starting point for a new task and fine-tune it. Then, the new detection head is combined with the backbone network of the pre-trained model, and the entire model is fine-tuned. This approach can reduce the computational resources required for fine-tuning since only newly added detection heads need to be fine-tuned.
• Data augmentation: Use data augmentation techniques to augment the training data set, thereby reducing the amount of data required for fine-tuning.

3. Natural Language Processing

#In natural language processing tasks, PEFT can reduce the use of computing resources through the following strategies :

• Layered fine-tuning: First, the language model is pre-trained on a larger data set, and then the model is fine-tuned layer by layer. This approach can reduce the computational resources required for fine-tuning because there are fewer fine-tuning times per layer.
• Fine-tuning the head: Use the head of the pre-trained model (i.e. the fully connected layer) as the starting point for the new task and fine-tune it. This approach is often more efficient than fine-tuning the entire model because the head often contains task-relevant information.
• Data augmentation: Use data augmentation techniques to augment the training data set, thereby reducing the amount of data required for fine-tuning.

In general, PEFT is a very practical deep learning model fine-tuning technology that can improve model performance and fine-tuning efficiency under limited computing resources. In practical applications, researchers can select appropriate strategies for fine-tuning based on the characteristics of the task and the limitations of computing resources to obtain the best results.

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