GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning
This addresses the inflexibility of manually designed architectures for multi-attribute learning, offering an automated solution for practitioners in this domain.
The paper tackles the problem of discovering inter-attribute correlation structures in deep multi-attribute learning by proposing GNAS, a greedy neural architecture search method that automatically finds optimal tree-like architectures, achieving effective and compact results on three benchmark datasets.
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.