Category Shift

Category shift, the phenomenon where the categories of data change between training and testing phases, is a significant challenge in machine learning, particularly impacting the robustness and generalizability of models. Current research focuses on developing methods to handle category shifts in various applications, including image classification, object detection, and natural language processing, often employing techniques like hierarchical context descriptions, category prototypes, and generative models to improve model adaptability and reduce reliance on extensive labeled data. Addressing category shift is crucial for building more reliable and adaptable AI systems capable of functioning effectively in real-world scenarios characterized by evolving data distributions and unforeseen categories.