Transfer Learning in AI: Improving Model Performance with Pertained Networks

Abstract

Transfer learning is a powerful technique in artificial intelligence that enhances model performance by leveraging pre-trained networks. Instead of training a model from scratch, which can be resource-intensive and time-consuming, transfer learning uses a model previously trained on a large dataset for a related task. This approach involves adapting the pre-trained model to the specific requirements of a new task by fine-tuning it on a smaller, task-specific dataset. The pre-trained network, having already learned general features from a vast amount of data, provides a strong starting point, thus accelerating convergence and improving accuracy. This method is particularly beneficial in scenarios where labeled data is scarce or expensive to obtain, making it a valuable tool for enhancing performance in diverse AI applications.