Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications
This is an incremental review that helps researchers and practitioners make deep learning more accessible by optimizing model fine-tuning.
This review tackles the computational and memory challenges of traditional fine-tuning by analyzing Parameter Efficient Fine-Tuning (PEFT) techniques, finding that they reduce computational load, speed up training, and lower memory usage across applications like text generation and medical imaging.
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.