利用辅助大任务学习标签不一致的多任务
Learning Multi-Tasks with Inconsistent Labels by using Auxiliary Big Task
摘要：
多任务学习是通过在任务间传递和利用共同知识来提高模型的性能。现有的MTL工作主要集中在多个任务（MT）之间的标签集通常是相同的，因此它们可以用于跨任务学习的情况下。虽然几乎很少有作品探索每个任务只有少量训练样本的情况，并且它们的标签集只是部分重叠甚至不重叠。学习这样的MT更具挑战性，因为这些任务之间的相关性信息较少。为此，我们提出了一个框架来学习这些任务，通过共同利用丰富的信息，从一个学习辅助大任务有足够多的类，以涵盖所有这些任务和这些部分重叠的任务之间共享的信息。在我们的实现中，使用相同的神经网络架构的学习辅助任务来学习个别任务，关键的想法是利用可用的标签信息，以自适应地修剪隐藏层神经元的辅助网络，以构建相应的网络为每个任务，同时伴随着一个联合学习跨个别任务。我们的实验结果表明，与国家的最先进的方法相比，它的有效性。
英文摘要：
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 more challenging because of less correlation information available among these tasks. For this, we propose a framework to learn these tasks by jointly leveraging both abundant information from a learnt auxiliary big task with sufficiently many classes to cover those of all these tasks and the information shared among those partially-overlapped tasks. In our implementation of using the same neural network architecture of the learnt auxiliary task to learn individual tasks, the key idea is to utilize available label information to adaptively prune the hidden layer neurons of the auxiliary network to construct corresponding network for each task, while accompanying a joint learning across individual tasks. Our experimental results demonstrate its effectiveness in comparison with the state-of-the-art approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
