Convolutional Neural Network Based Framework

Convolutional neural networks (CNNs) are increasingly used to build frameworks for diverse applications, ranging from analyzing animal behavior and classifying medical images to controlling complex systems and improving image-based localization. Current research focuses on enhancing CNN architectures through techniques like knowledge distillation for efficient model deployment, incorporating auxiliary information for improved accuracy, and integrating CNNs with other methods such as transformers and geometric constraints to leverage complementary strengths. These frameworks offer significant potential for advancing various fields, enabling more efficient and accurate solutions in areas like precision agriculture, communications, robotics, and healthcare.