Squeeze Flow

Squeeze flow, in the context of recent research, refers to techniques that efficiently compress and selectively utilize information within neural networks, aiming to improve performance and reduce computational demands. Current research focuses on developing novel architectures like Squeeze-and-Excitation (SE) blocks and their variations, which dynamically emphasize important features, and on applying these methods to diverse applications including image processing, medical imaging, and natural language processing. These advancements are significant because they enable the deployment of powerful models on resource-constrained devices and improve the efficiency and accuracy of various machine learning tasks.