A two-stage multi-task training method that trains auxiliary tasks equally in task-specific decoders and weights their shared-encoder gradients by uncertainty and gradient norm improves primary-task performance relative to prior auxiliary-task weighting methods.
Learning multiple tasks with multilinear relationship networks,
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Unprejudiced Training Auxiliary Tasks Makes Primary Better: A Multi-Task Learning Perspective
A two-stage multi-task training method that trains auxiliary tasks equally in task-specific decoders and weights their shared-encoder gradients by uncertainty and gradient norm improves primary-task performance relative to prior auxiliary-task weighting methods.