Single Convolutional

Single convolutional neural networks (CNNs) are emerging as powerful yet efficient tools across diverse applications, focusing on minimizing model complexity while maximizing performance. Research emphasizes streamlined architectures, often employing a single convolutional layer coupled with other techniques like knowledge distillation, structural re-parameterization, or specialized convolutional operations (e.g., sorting convolutions) to achieve competitive results in tasks ranging from image classification and style transfer to medical diagnosis and activity recognition. This approach offers significant advantages in resource-constrained environments and facilitates faster inference, making single-convolutional CNNs increasingly relevant for real-world deployment in various fields.