Inter Class

Inter-class relationships, referring to the distinctions and interactions between different classes of data, are a crucial focus in machine learning research. Current efforts concentrate on improving model performance by explicitly leveraging inter-class information, for example, by maximizing the separation between classes in feature space or by strategically manipulating inter-class connections in graph-structured data. This is achieved through techniques like novel loss functions, multi-task architectures within diffusion models, and the development of surrogate models that better preserve inter-class distinctions. Understanding and effectively managing inter-class dynamics is vital for enhancing the robustness and accuracy of various machine learning applications, including image segmentation, open-set recognition, and adversarial attack mitigation.