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, offering a detailed comparison of various strategies highlighting applications across different domains, including text generation, medical imaging, protein modeling, and speech synthesis. By assessing the effectiveness of PEFT methods in reducing computational load, speeding up training, and lowering memory usage, this paper contributes to making deep learning more accessible and adaptable, facilitating its wider application and encouraging innovation in model optimization. Ultimately, the paper aims to contribute towards insights into PEFT's evolving landscape, guiding researchers and practitioners in overcoming the limitations of conventional fine-tuning approaches.

翻译：深度学习的崛起在计算机视觉、自然语言处理和医学影像等领域取得了显著进展，这主要得益于针对特定任务的预训练模型适配。传统微调方法需要调整所有参数，面临着高昂的计算和内存需求挑战。为此，参数高效微调（PEFT）技术应运而生，通过选择性更新参数来平衡计算效率与性能。本综述系统考察了PEFT方法，详细比较了不同策略在文本生成、医学影像、蛋白质建模和语音合成等多个领域的应用效果。通过评估PEFT方法在降低计算负载、加速训练和减少内存占用方面的有效性，本文助力提升深度学习的易用性和适应性，推动其更广泛的应用并促进模型优化创新。最终，本文旨在为理解PEFT的发展趋势提供深刻见解，指导研究人员和实践者克服传统微调方法的局限性。