Multi Task Fusion

Multi-task fusion aims to combine the outputs of multiple, individually trained machine learning models to achieve a more comprehensive and robust result than any single model could provide. Current research focuses on improving fusion strategies, particularly using reinforcement learning and attention mechanisms to effectively weigh and integrate diverse information sources, as well as developing efficient methods for merging and compressing the resulting multi-task models. This approach holds significant promise for enhancing performance in various applications, including recommender systems, computer vision (e.g., panoptic segmentation), and medical image analysis, by leveraging the strengths of multiple specialized models.