Multi Task Learning Framework

Multi-task learning (MTL) frameworks aim to improve the efficiency and performance of machine learning models by training a single model to perform multiple related tasks simultaneously. Current research focuses on developing novel architectures, often incorporating transformers or graph neural networks, and sophisticated loss functions to effectively handle diverse tasks and data characteristics, including techniques like knowledge distillation and dynamic adaptive optimization. This approach is proving valuable across numerous domains, from speech and audio processing to natural language processing and medical image analysis, leading to improved model generalization and resource efficiency. The resulting models often achieve state-of-the-art performance on individual tasks while requiring less training data compared to single-task approaches.