Multitask Learning Framework

Multitask learning frameworks aim to improve efficiency and accuracy by training a single model to perform multiple related tasks simultaneously, rather than training separate models for each. Current research focuses on adapting existing architectures like YOLOv8 and Perceiver, as well as exploring novel approaches, to handle diverse data types including images, tabular data, and audio signals for tasks such as object detection, risk stratification, and sentiment analysis. This approach offers advantages in data efficiency, reduced computational cost, and improved performance compared to single-task learning, with applications spanning diverse fields from healthcare and infrastructure assessment to robotics and social media analysis.