Deep Transfer Learning

Deep transfer learning leverages pre-trained deep learning models to improve performance on tasks with limited data, addressing a major bottleneck in many fields. Current research focuses on applying this technique across diverse domains, including medical image analysis (using architectures like ResNet, VGG, DenseNet, and Vision Transformers), signal processing (employing CNNs and LSTMs), and natural language processing (utilizing BERT and other transformer models). This approach significantly enhances the efficiency and effectiveness of deep learning, impacting various applications from disease diagnosis and structural health monitoring to autonomous driving and personalized medicine.