Learning Multi-Tasks with Inconsistent Labels by using Auxiliary Big Task. (arXiv:2201.02305v1 [cs.LG])

Multi-task learning is to improve the performance of the model by
transferring and exploiting common knowledge among tasks. Existing MTL works
mainly focus on the scenario where label sets among multiple tasks (MTs) are
usually the same, thus they can be utilized for learning across the tasks.
While almost rare works explore the scenario where each task only has a small
amount of training samples, and their label sets are just partially overlapped
or even not. Learning such MTs is …

arxiv big learning