ConvBN Block

ConvBN blocks, fundamental building blocks in convolutional neural networks, are being actively researched to improve efficiency and stability across various applications. Current efforts focus on optimizing ConvBN block behavior across different operational modes (training, evaluation, deployment), particularly for transfer learning, and adapting them for specific tasks like semantic segmentation within architectures such as U-Nets. These optimizations aim to reduce computational costs and memory footprint while maintaining or improving performance, leading to more efficient and practical deep learning models for computer vision and beyond.