Deep Feature

Deep features, high-level representations extracted from intermediate layers of deep neural networks, are increasingly used to improve various machine learning tasks. Current research focuses on leveraging these features for diverse applications, including image classification, object detection, and medical image analysis, often employing architectures like convolutional neural networks (CNNs) and transformers, and incorporating techniques such as transfer learning and feature fusion. The ability of deep features to capture complex patterns and relationships within data significantly enhances model performance and enables novel approaches in fields ranging from medical diagnosis to remote sensing and autonomous driving.