Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation

Previous state-of-the-art deep generative models improve fine-grained image generation quality by designing hierarchical model structures and synthesizing images across multiple stages. The learning process is typically performed without any supervision in object categories. To address this issue, while at the same time to alleviate the level of complexity of both model design and training, we propose a Single-Stage Controllable GAN (SSC-GAN) for conditional fine-grained image synthesis in a semi-supervised setting. Considering the fact that fine-grained object categories may have subtle distinctions and shared attributes, we take into account three factors of variation for generative modeling: class-independent content, cross-class attributes and class semantics, and associate them with different variables. To ensure disentanglement among the variables, we maximize mutual information between the class-independent variable and synthesized images, map real data to the latent space of a generator to perform consistency regularization of cross-class attributes, and incorporate class semantic-based regularization into a discriminator's feature space. We show that the proposed approach delivers a single-stage controllable generator and high-fidelity synthesized images of fine-grained categories. SSC-GAN establishes state-of-the-art semi-supervised image synthesis results across multiple fine-grained datasets.