<![CDATA[Deep neural networks (DNNs) have achieved remarkable success in various domains; however, their performance often relies heavily on large-scale, high-quality labeled datasets, which are scarce in low-resource environments. Cross-domain transfer learning has emerged as a promising technique for adapting pre-trained models from data-rich source domains to low-resource target domains to address this limitation. This study explores innovative strategies to enhance the performance and applicability of DNNs through cross-domain transfer learning, focusing on challenges such as domain disparity, data scarcity, and computational constraints. We evaluate several transfer learning approaches, including feature-based transfer, parameter fine-tuning, and adversarial domain adaptation, across diverse healthcare, agriculture, and natural language processing applications. Experimental results demonstrate significant improvements in model accuracy and generalization in low-resource environments, with accuracy gains of up to 20% compared to models trained from scratch. Additionally, we analyze the impact of transfer learning on reducing training time and computational requirements, making it a viable solution for resource-constrained settings. Despite its potential, the study highlights critical challenges, including negative transfer, model interpretability, and ethical considerations in domain transfer. Addressing these issues, we propose a framework for selecting optimal source domains and enhancing model robustness through hybrid techniques and unsupervised learning. This research emphasizes the transformative potential of cross-domain transfer learning in bridging the gap between data-rich and low-resource environments, paving the way for more equitable and efficient applications of deep learning technologies worldwide.]]>
