In machine learning, the relatedness across multiple tasks is usually complex and entangled. Due to dataset bias, the relatedness among tasks might be distorted and mislead the training of the models with solid learning ability, such as the multi-task neural networks. In this paper, we propose the idea of Relatedness Refinement Multi-Task Learning (RRMTDL) by introducing adversarial learning in the multi-task deep neural network to tackle the problem. The RRMTDL deep learning model restrains the misleading relatedness task by adversarial training and extracts information sharing across tasks with valuable relatedness. With RRMTDL, multi-task deep learning can enhance the task-specific representation for the major tasks by excluding the misleading relatedness. We design tests with various combinations of task-relatedness to validate the proposed model. Experimental results show that the RRMTDL model can effectively refine the task relatedness and prominently outperform other multi-task deep learning models in datasets with entangled task labels.

In the attribute recognition area, attributes that are unrelated in the real world may have a high co-occurrence rate in a dataset due to the dataset bias, which forms a misleading relatedness. A neural network, especially a multi-task neural network, trained on this dataset would learn this relatedness, and be misled when it is used in practice. In this paper, we propose Share-and-Compete Multi-Task deep learning (SCMTL) model to handle this problem. This model uses adversarial training methods to enhance competition between unrelated attributes while keeping sharing between related attributes, making the task-specific layer of the multi-task model to be more specific and thus rule out the misleading relatedness between the unrelated attributes. Experiments performed on elaborately designed datasets show that the proposed model outperforms the single task neural network and the traditional multi-task neural network in the situation mentioned above.