  A key problem in deep multi-attribute learning is to effectively discover the
inter-attribute correlation structures. Typically, the conventional deep
multi-attribute learning approaches follow the pipeline of manually designing
the network architectures based on task-specific expertise prior knowledge and
careful network tunings, leading to the inflexibility for various complicated
scenarios in practice. Motivated by addressing this problem, we propose an
efficient greedy neural architecture search approach (GNAS) to automatically
discover the optimal tree-like deep architecture for multi-attribute learning.
In a greedy manner, GNAS divides the optimization of global architecture into
the optimizations of individual connections step by step. By iteratively
updating the local architectures, the global tree-like architecture gets
converged where the bottom layers are shared across relevant attributes and the
branches in top layers more encode attribute-specific features. Experiments on
three benchmark multi-attribute datasets show the effectiveness and compactness
of neural architectures derived by GNAS, and also demonstrate the efficiency of
GNAS in searching neural architectures.
