## 1 Introduction  
Attribute recognition, which is the problem of finding the hidden factors composing the attributes from a given input and recognizing the pattern of attributes, is one of the main topics receiving large attention in the field of pattern recognition. Along with the development of machine learning technologies, for extracting various attributes from a single dataset, the demands for recognizing multiple attributes from one input has also increased, which is called multi-attribute recognition. Though the classic approaches to attribute recognition have been applied to develop a well-designed system for a single attribute [1; 2; 3; 4; 5; 6], multi-attribute recognition requires more sophisticated machine learning methods such as deep network and multi-task learning. Multi-task learning is one of the transfer learning methods that improves recognition performance of each task by training more than two related tasks simultaneously in a single network [7]. The concept of multi-task learning is to find common features that can be beneficial to each task. Through multi-task learning, it is expected that a system capable of recognizing and analyzing various attributes is achieved, along with performance improvement and computational cost reduction. In this regard, multi-task learning plays an important role in multi-attribute recognition. However, as mentioned in [7], multi-task learning does not always guarantee the performance improvement of all tasks, and the tasks to be learned together should be related to each other. Both [8; 9] also reported that the key of improving performance of all tasks by multi-task learning is selecting tasks that are related to each other. Many conventional studies of multi-attribute recognition [10; 11; 12; 13] have a limitation in that they do not take into account the mutual relationship between attributes, assuming that the attributes are independent of each other. In real world problems, however, attributes are conceptually related to each other. For instance, a person wearing a skirt is more likely to be a woman than a man. This means that a task recognizing a person's attire can positively affect a gender recognition task. Thus, through applying the relationship among attributes to multi-attribute recognition, it is expected that recognition performance will improve. There have been several studies on multi-attribute recognition using multi-task learning, but most of them have been data-dependent. The deep learning based method for recognizing pedestrian attributes introduced by [PERSON] et al. [14] is dependent on the portion of data and does not take into account the relationship between attributes. [PERSON] and [PERSON] [15] introduced a multi-column CNN(Convolutional Neural Network) that adopt implicit and explicit attribute relationships for facial attribute classification. Though it considers the relationship between attributes, it is only applicable to specific facial attributes since it requires prior knowledge on the relationship between input area and attributes for region-based grouping. In this paper, we propose two multi-attribute recognition methods which use a novel output representation of a deep network based on the relationship among attributes. For considering exclusive relationship among attributes, we first compose the recognition tasks by grouping the attributes that are in a mutually exclusive relationship. For example, based on the fact that the attribute of \"male\" is mutually exclusive to the attribute of \"female\", we compose a task for \"gender recognition\". Similarly, using the fact that identity of each subject is also exclusive to the identities of other subjects, \"identity recognition\" can also be composed as single task. Through the grouping process, we obtained a number of recognition tasks such as gender recognition, expression recognition, race recognition and so on.