An Optimized Algorithmic Frameworks for Transfer Learning to Enhance Efficiency and Accuracy in Pretrained Model Adaptation

Existing transfer learning systems often face challenges such as overfitting, domain mismatch, and inefficient adaptation to new tasks, limiting their scalability in diverse applications. To address these issues, we propose TransferOptNet, an optimized algorithmic framework for transfer learning with pretrained models. The framework incorporates adaptive fine-tuning, domain-specific feature extraction, and a hybrid optimization strategy. TransferOptNet is tested on three different datasets: ImageNet, CIFAR-100, and Sentiment140. TransferOptNet outperformed the ResNet-50, VGG-16, and BERT on the tested datasets with accuracies of 91.2%, 87.5%, and 89%. These results highlight the effectiveness and scalability of TransferOptNet for crossdomain tasks in image and text data processing.