Multi-task Loss

Multi-task loss is a type of loss function used in machine learning models that are designed to perform multiple tasks simultaneously. In multi-task learning, a model is trained to perform several related tasks at the same time, rather than training separate models for each task. The multi-task loss function combines the individual losses for each task into a single, overall loss function that the model tries to minimize during training. The goal is to find a set of model parameters that simultaneously optimize all of the tasks, rather than optimizing each task independently. Multi-task learning can be useful in situations where the tasks are related or share common features, as it can lead to improved performance and faster training times compared to training separate models for each task.