## 2 Related Work  
**Multi-task learning. ** [PERSON] [19] first analyzed multi-task learning in detail. Since then, multi-task learning has been adopted for solving different computer vision problems. [PERSON] et al. used a convolutional neural network (CNN) for pose prediction and action classification of people in unconstrained images [20]. [PERSON] et al. proposed a multi-scale convolutional architecture for predicting depth, surface normals and semantic labels [21]. [PERSON] et al. presented cross-stitch units to learn shared representations for multi-task learning in ConvNets [22]. [PERSON] et al. presented a CNN that jointly handles low-, mid-, and high-level vision tasks in a unified architecture [23]. [PERSON] et al. studied a method for performing multiple tasks in a single deep neural network by iteratively pruning and packing the network parameters [24]. [PERSON] et al. proposed a novel architecture containing multiple networks of different configurations termed deep virtual networks with respect to different tasks and memory budgets [25]. Recently, multi-task learning with DCNNs have also been studied and applied to face attributes prediction. [PERSON] et al. used a deep convolutional neural network(DCNN) for age and gender classification [26]. [PERSON] et al. proposed a novel deep learning framework for attribute prediction in the wild [27]. [PERSON] et al. presented a DCNN for face analysis utilizing transfer learning from a face recognition model [28]. [PERSON] et al. proposed a method to multi-attribute recognition of facial images based on a deep learning network that automatically learns the exclusive and joint relationship among attribute recognition tasks [29]. In multi-task learning, when the prediction of one task which will be used as condition is accurate, other tasks can be formulated by using conditional probability. For example, in [30], the experimental results on the MORPH-II dataset show that the multitask method achieves 98% gender recognition accuracy, thus the age probability \(P(A(X)=a)\) can be calculated using the gender-conditioned probability \(P(A(X)=a\mid G(X)=g)\) and the marginal gender probability \(P(G(X)=g)\) in their proposed conditional multitask learning method. However, the error predicted gender \(G(X)=g\) will lead to incorrect calculation of \(P(A(X)=a\mid G(X)=g)\) and \(P(G(X)=g)\); therefore, their method cannot be used when the multitask method cannot predict gender accurately on other datasets. **Attention mechanism. ** Human perception is similar to the attention mechanism that selects specific parts of the input information, rather than using all input information. In neural networks, attention mechanism can be used as feature selectors that can determine the importance of each feature for the particular task. The attention mechanism has been studied and applied to recurrent neural networks (RNNs) and long short term memory (LSTM) for sequential tasks [31; 32; 33]. The attention mechanism with DCNNs have been applied to vision-related tasks. [PERSON] et al. proposed a deep-learning based generative framework with visual attention [34]. [PERSON] et al. applied visual attention to fine-grained classification task using DCNN [35]. [PERSON] et al. presented an attention based model that automatically learns to describe the content of images [36]. [PERSON] et al. proposed a diversified visual attention network for fine-grained object classification [37]. Inspired by the attention mechanism, we propose a multi-task learning using task dependencies architecture for face attributes prediction in this paper. The main contributions of this paper are summarized as follows:  
1. A multi-task learning using task dependencies architecture for face attributes prediction in end-to-end manner. The designed attention modules in our proposed architecture are used for learning task-dependent disentangled representations. We evaluate the performance with the tasks of smile and gender prediction. 2.