# PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation
## 2 Related Work
### Fine-Tuning Models  
In the field of machine learning, fine-tuning models have been widely employed in transfer learning tasks. Fine-tuning refers to the process of further training a neural network model that has already undergone pre-training, using data specific to a particular task. The aim is to adapt the model more effectively to the new task, thereby enhancing its performance.  
Adapters are lightweight structures introduced into the model, allowing specific task fine-tuning on a pre-trained model without modifying the overall architecture. Adapters provide a means of preserving generality while facilitating flexible fine-tuning for specific tasks. For example, the GPT [28] model proposed by OpenAI is a pre-trained generative model based on the Transformer architecture. Widely employed in natural language processing tasks such as text generation, text classification, and dialogue generation, GPT demonstrates powerful language understanding and generation capabilities. During the pre-training phase, GPT learns general language representations through unsupervised learning on massive text data. Subsequently, it adapts to specific downstream tasks through parameter fine-tuning. ViT-Adapter [29] is an approach that introduces adapter layers into the Visual Transformer (ViT) model, allowing for customized task fine-tuning on pre-trained models to enhance performance on specific visual tasks. SAM [13] is a universal image segmentation model proposed by [PERSON] et al. Pre-trained on over 11 million images and utilizing over a billion masks, SAM has demonstrated outstanding performance in image segmentation tasks. This success highlights the importance of large-scale data and powerful pre-training in the field of deep learning. These research achievements collectively drive the development of computer vision, providing more powerful and flexible solutions for various application scenarios. SAM segments targets through single points or borders for target hints, and its excellent segmentation performance is attributed to the fact that its pre-training data mostly have clear and regular boundaries.  
However, in medical images, the boundaries of tissues and content are often fuzzy, rendering SAM unable to accurately identify target content through local information alone. To enhance SAM's adaptability to downstream tasks, some researchers have employed parameter fine-tuning techniques to improve its performance. MedSAM [30] utilizes parameter-efficient fine-tuning (PEFT) to fine-tune the pre-trained SAM model, demonstrating excellent performance in medical image segmentation. SAM-Med 2D [14] introduces adapters into the encoder for fine-tuning, making it well-suited for the medical domain. The Medical SAM Adapter [16] retains most parameters of the SAM model and applies popular adapters from NLP techniques to medical image segmentation, showcasing surprisingly good performance.  
Despite their success, fine-tuning models encounter challenges, including the need for large-scale annotated data, high computational costs, and the requirement for adaptability in new domains. Fine-tuning may also face constraints due to conceptual differences during domain transfer, necessitating careful adjustments to accommodate varying tasks and data distributions.