Branch Network

Branch networks, characterized by parallel processing pathways, are a prevalent architecture in various machine learning applications, primarily aiming to improve model performance by integrating complementary information sources or handling data imbalances. Current research focuses on dual-branch and even tri-branch designs, often incorporating convolutional neural networks, transformers, or graph neural networks, depending on the specific task (e.g., image recognition, 3D shape measurement, action recognition). These advancements demonstrate significant improvements in accuracy and efficiency across diverse fields, including medical image analysis, computer vision, and electronic design automation, highlighting the versatility and impact of this architectural approach.