Level Interaction

Level interaction in machine learning focuses on improving model performance by strategically incorporating interactions between different levels of data representation or different tasks. Current research emphasizes multi-level interactions, often employing techniques like attention mechanisms, transformer networks, and multi-task learning frameworks to capture relationships between features, instances, and classes, or between different views of the same data (e.g., left and right views in stereo imaging). These advancements are improving the robustness and accuracy of models across diverse applications, including image restoration, user growth prediction, and semi-supervised learning, by leveraging richer contextual information. The resulting improvements in model accuracy and efficiency have significant implications for various fields, from computer vision to personalized recommendations.