Transfer Learning for Computer Vision

Abstract

Computer vision has benefited from deep learning, making it possible to create complex systems. Computer vision has seen radical changes using deep learning techniques, specifically transfer learning for computer vision. It enables computers to understand and interpret visual data, such as images and videos, with great precision. Transfer learning in particular, is a deep learning technique that has transformed several areas of computer vision, including face recognition, semantic segmentation, object detection, and image categorization. This powerful technology is essential in applications such as autonomous vehicles, healthcare, surveillance, and more, since it has improved our capacity to identify, locate, and classify objects in images as well as comprehend complex visual scenes. In computer vision, transfer learning is a method that allows us to use previously taught models to tackle new problems. Benefits include shortened training times and efficient feature extraction. This chapter provides a brief help for implementation of transfer learning for computer vision.