A Graph-Based Discriminator Architecture for Multi-Attribute Facial Image Editing

Abstract

Multi-attribute editing aims to synthesize new facial images with multiple desired attributes while at the same time preserving other contents. Generative Adversarial Networks (GANs) with encoder-decoder-based generators are typically applied to this task, while the co-occurrence nature of attributes is overlooked by generic discriminators when identifying real and synthesized instances. To address the issue, we focus on precisely capturing semantics associated with target attributes in this work, and propose a Graph-based Discriminator architecture for a GAN model, which is referred to as GD-GAN, for explicitly modeling and leveraging the attribute dependencies. Specifically, the co-occurrence ratio between attributes is used to build a correlation matrix, which captures inter-attribute relationships. We design a discriminator with a Graph Convolutional Network (GCN) to integrate knowledge about the attribute dependencies into the adversarial training process. Different from the existing methods that identify the synthesized data conditioned on the attributes individually, we leverage the attribute correlations by performing feature propagation over the graph of attributes, which leads to interdependent representations for real-fake instance identification. Incorporating the relationships of attributes eventually induces the generator to capture precise semantics associated with the attributes. Empirical results on multiple benchmarks demonstrate the superior performance of GD-GAN in high-quality semantic manipulation.