Advanced Transfer Learning Strategies for Improving Deep Learning Model Efficiency and Accuracy

Machine learning primarily focuses on supervised learning, which relies on labeled data, while unsupervised learning addresses data without labels. Reinforcement learning, on the other hand, develops agents that learn optimal actions based on past experiences. Transfer learning, distinctively, enhances learning in a target task by leveraging knowledge from related source tasks. In recent years, many machine learning models have remained single-task focused. This paper explores scenarios in which transfer learning can be applied effectively from a source domain to a target domain, employing feature selection, extraction, and construction techniques. These methods enable transformation into a new feature space by generating a refined subset of the original features.