Multi Task Loss

Multi-task loss is a machine learning technique that optimizes a model to perform multiple tasks simultaneously, improving efficiency and generalization compared to training separate models for each task. Current research focuses on developing effective multi-task loss functions, often within deep learning architectures like convolutional neural networks and gradient boosting machines, and exploring how to best share information between tasks (e.g., through shared encoders or decision trees). This approach is proving valuable across diverse applications, from image classification and remote sensing to medical prognosis and natural language processing, by enabling more robust and efficient models, particularly when data is limited or noisy.