Excluding the Misleading Relatedness Between Attributes in Multi-Task Attribute Recognition Network

Abstract

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.