Partially Shared Multi-Task Convolutional Neural Network with Local
Constraint for Face Attribute Learning
Abstract
In this paper, we study the face attribute learning problem by considering the identity information and attribute
relationships simultaneously. In particular, we first introduce a Partially Shared Multi-task Convolutional Neural Network (PS-MCNN), in which four Task Specific Networks (TSNets) and one Shared Network (SNet) are connected by Partially Shared (PS) structures to learn better
shared and task specific representations. To utilize identity information to further boost the performance, we introduce a local learning constraint which minimizes the difference between the representations of each sample and its
local geometric neighbours with the same identity. Consequently, we present a local constraint regularized multitask network, called Partially Shared Multi-task Convolutional Neural Network with Local Constraint (PS-MCNNLC), where PS structure and local constraint are integrated
together to help the framework learn better attribute representations. The experimental results on CelebA and LFWA
demonstrate the promise of the proposed methods.