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Multi-task learning is to improve the performance of the model bytransferring and exploiting common knowledge among tasks. Existing MTL worksmainly focus on the scenario where label sets among multiple tasks (MTs) areusually 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 smallamount of training samples, and their label sets are just partially overlappedor even not. Learning such MTs is more challenging because of less correlationinformation available among these tasks. For this, we propose a framework tolearn these tasks by jointly leveraging both abundant information from a learntauxiliary big task with sufficiently many classes to cover those of all thesetasks and the information shared among those partially-overlapped tasks. In ourimplementation of using the same neural network architecture of the learntauxiliary task to learn individual tasks, the key idea is to utilize availablelabel information to adaptively prune the hidden layer neurons of the auxiliarynetwork to construct corresponding network for each task, while accompanying ajoint learning across individual tasks. Our experimental results demonstrateits effectiveness in comparison with the state-of-the-art approaches.

Learning Multi-Tasks with Inconsistent Labels by using Auxiliary Big Task