Deep Learning Enhancements Using Pretraining and Fine-Tuning

  • Authors

    • Dr. Kwame Nkosi Department of Economics, University of Ghana, Accra, Ghana. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2023PII0Q6W

    Published 07-04-2023

  • Deep Learning, Pretraining, Fine-Tuning, Transfer Learning, Self-Supervised Learning, Representation Learning, Neural Networks, Foundation Models

    Issue

    Vol. 6 No. 2 (2023)

    Section

    Articles

    How to Cite

    [1]
    K. Nkosi, “Deep Learning Enhancements Using Pretraining and Fine-Tuning”, IJADSMC, vol. 6, no. 2, pp. 01–12, Jul. 2023, doi: 10.67228/30713498/IJADSMC-2023PII0Q6W.

  • Abstract

    Deep learning has achieved state-of-the-art performance across domains such as computer vision, NLP, and healthcare, but it often requires large datasets and high computational resources. Pretraining and fine-tuning have emerged as effective strategies to improve performance, reduce training cost, and enhance generalization. Pretraining learns transferable representations from large-scale data, while fine-tuning adapts models to specific tasks with limited labeled data. This paper provides a comprehensive study of various pretraining methods (supervised, unsupervised, self-supervised) and fine-tuning techniques, including full-model and parameter-efficient approaches. A unified framework is proposed to integrate both processes in a deep learning pipeline. Experimental findings show that pretrained models outperform those trained from scratch in terms of accuracy, convergence speed, and robustness. The study also highlights challenges, trade-offs, and emerging trends such as foundation models and multimodal learning, emphasizing the importance of these techniques in advancing deep learning systems.

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