Convolutional Neural Networks (CNNs) have demonstrated remarkable success across diverse visual recognition tasks, yet their performance is often constrained by the availability of large labeled datasets and extensive computational resources. Transfer learning has emerged as a pivotal paradigm to mitigate these limitations by leveraging knowledge from pre-trained networks to new target tasks, enabling accelerated convergence and improved generalization. This review systematically examines the principal strategies employed in CNN-based transfer learning, including feature extraction, full-network and layer-wise fine-tuning, and domain adaptation techniques that address shifts between source and target distributions. Key challenges inherent in these methodologies are analyzed, encompassing overfitting on limited target datasets, catastrophic forgetting during adaptation, and the computational overhead associated with large-scale fine-tuning. Recent advancements are highlighted, with emphasis on parameter-efficient approaches such as low-rank adaptation and adapter modules, self-supervised pre-training frameworks that reduce dependency on labeled data, and cross-domain transfer strategies that extend applicability to heterogeneous tasks. Through a comparative synthesis of existing techniques, this review elucidates their strengths, limitations, and applicability across benchmark datasets including ImageNet, CIFAR, and specialized medical imaging collections. The work ultimately identifies critical gaps and emerging directions for future research, providing a comprehensive foundation for the design of efficient, robust, and generalizable transfer learning frameworks in CNNs.

Convolutional Neural Networks (CNNs), Transfer Learning, Fine-Tuning Strategies, Feature Extraction, Domain Adaptation, Parameter-Efficient Tuning, Self-Supervised Pretraining, Cross-Domain Transfer