Multi-Task Learning

Multi-task learning studies how one model can learn several related predictions at once, using shared information to improve accuracy, data efficiency, or deployment cost. Common designs combine a shared encoder with task-specific heads, adapters, or routed experts; other methods balance losses, adjust conflicting gradients, or group tasks by affinity. Applications range from dense vision benchmarks such as NYUv2 and Cityscapes to language, medical prediction, recommendation, and scientific modeling. The central tension is positive versus negative transfer: sharing can help when tasks have useful common structure, but differences in data, objectives, or capacity can cause gains for some tasks and losses for others.