Push Pull

"Push-pull" describes a class of methods employing complementary, often opposing, operations to enhance performance in diverse machine learning tasks. Current research focuses on developing push-pull algorithms for robust distributed optimization, improving accuracy in link prediction and anomaly detection using positive-unlabeled learning, and enhancing the precision of image and video processing through innovative network architectures like PushPull-Conv and PnPNet. These techniques are proving valuable in improving the robustness and efficiency of machine learning models across various applications, from computer vision and robotics to network security and data analysis.