Multi Task Classification

Multi-task classification aims to simultaneously learn multiple related classification tasks, leveraging shared representations to improve efficiency and generalization performance compared to training individual models. Current research focuses on addressing challenges like harmful task interference through techniques such as sample-level weighting and on improving knowledge transfer between tasks, particularly in scenarios with category shifts, using methods like association graph learning. These advancements are impacting diverse fields, from cultural heritage analysis (using multimodal data) to medical image analysis and audio event detection, improving accuracy and efficiency in complex classification problems.