Task Loss

Task loss, central to multi-task learning (MTL), focuses on optimizing the balance between individual task objectives within a shared neural network to prevent suboptimal performance due to conflicting gradients or noisy data. Current research emphasizes developing sophisticated weighting methods, such as uncertainty-based or excess risk-based approaches, and novel optimization algorithms (e.g., gradient manipulation techniques) to achieve better task balancing and Pareto optimality. These advancements aim to improve the efficiency and effectiveness of MTL across diverse applications, from manufacturing process optimization to natural language understanding, by enabling more robust and accurate model training.