基于条件的边界平衡生成对抗网络

Conditional boundary equilibrium generative adversarial network

目前没有能够使用简单网络结构生成高质量特定图像的生成模型,针对这一项任务,结合边界平衡生成对抗网络(boundary equilibrium generative adversarial network,BEGAN)的优点,添加附加条件特征以及均方误差损失,建立了条件边界平衡生成对抗网络(conditional-BEGAN,C-BEGAN),使用这种方法提取其中的生成模型用于特定图像的生成,实验结果表明,该方法相比于其他监督类生成模型可以使用更简单的网络达到更快的收敛速度,并且能够生成具有更好质量以及多样性的图片。

At present,there is no generation model that can use simple network structure to generate high-quality specific images.For this task,this paper established the conditional boundary equilibrium generative adversarial network by combining the advantages BEGAN and adding additional condition features and the MSE loss.It used this method to extract the generation model for specific image generation.Experimental results show that compared with other supervised class generation models,this method can use simpler networks to achieve faster convergence speed and generate images with better quality and diversity.