Frozen Pre Trained

Frozen pre-trained models are leveraging the power of large pre-trained networks by adapting them to new tasks with minimal retraining, focusing on efficiency and mitigating issues like bias and overfitting. Current research emphasizes parameter-efficient fine-tuning methods, such as LoRA and adapters, often applied within architectures like Vision Transformers and large language models, to achieve high performance with limited computational resources. This approach is significantly impacting various fields, from image processing (pansharpening, object detection) and natural language processing (text generation, sentiment analysis) to time series analysis and even medical image analysis, by enabling the application of powerful models to datasets that would otherwise be too small or computationally expensive to train from scratch.