Transfer Learning in Image Recognition: Leveraging Pre-trained Models for Improved Performance

Abstract

In recent years, image recognition has witnessed significant advancements, owing to the rise of deep learning techniques. Transfer learning, a subfield of machine learning, has emerged as a powerful strategy to boost the performance of image recognition models. This paper explores the application of transfer learning in the context of image recognition, with a focus on leveraging pre-trained models to enhance overall performance. The primary objective of this research is to investigate the effectiveness of transfer learning in improving the accuracy and efficiency of image recognition tasks. We delve into the underlying principles of transfer learning, examining how knowledge gained from one domain can be transferred to another. Specifically, we explore the use of pre-trained convolutional neural networks (CNNs) and their ability to capture generic features that are transferable across diverse datasets. The experimental methodology involves the utilization of popular pre-trained models, such as VGG16, ResNet, and MobileNet, on benchmark image datasets.