Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications

arXiv:2404.13506v1 Announce Type: new
Abstract: The rise of deep learning has marked significant progress in fields such as computer vision, natural language processing, and medical imaging, primarily through the adaptation of pre-trained models for specific tasks. Traditional fine-tuning methods, involving adjustments to all parameters, face challenges due to high computational and memory demands. This has led to the development of Parameter Efficient Fine-Tuning (PEFT) techniques, which selectively update parameters to balance computational efficiency with performance. This review examines PEFT approaches, …

abstract analysis applications arxiv challenges computational computer computer vision cs.ai cs.cl cs.lg deep learning face fields fine-tuning imaging language language processing medical medical imaging natural natural language natural language processing parameters pre-trained models processing progress specific tasks tasks through type vision