  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.
