Pre Trained Model

Pre-trained models are foundational large-scale models trained on massive datasets, subsequently adapted for specific downstream tasks using techniques like fine-tuning or parameter-efficient fine-tuning (PEFT). Current research emphasizes improving the efficiency and effectiveness of these adaptation methods, exploring architectures such as Vision Transformers and diffusion models, and developing algorithms like LoRA and its nonlinear extensions to minimize resource consumption while maximizing performance. This field is crucial for advancing various applications, from medical image analysis and environmental sound classification to autonomous driving and natural language processing, by enabling the development of high-performing models with limited data and computational resources.