Image content is a predominant factor in marketing campaigns, websites and banners. Today, marketers and designers spend considerable time and money in generating such professional quality content. We take a step towards simplifying this process using Generative Adversarial Networks (GANs). We propose a simple and novel conditioning strategy which allows generation of images conditioned on given semantic attributes using a generator trained for an unconditional image generation task. Our approach is based on modifying latent vectors, using directional vectors of relevant semantic attributes in latent space. Our method is designed to work with both discrete (binary and multi-class) and continuous image attributes. We show the applicability of our proposed approach, named Directional GAN, on multiple public datasets, with an average accuracy of 86.4% across different attributes.
翻译:图像内容是营销运动、网站和横幅的主导因素。今天,营销者和设计者花费大量时间和金钱来制作这种专业质量内容。我们采取一个步骤来简化这一过程,使用创能反对网络(GANs)来简化这一过程。我们提出了一个简单而创新的调整战略,允许使用经过培训的无条件图像生成任务的发电机,根据特定语义属性生成图像。我们的方法是利用潜空相关语义属性的方向矢量来改变潜在矢量。我们的方法旨在与离散(二等和多等)和连续图像属性一起工作。我们用多种公共数据集展示了我们拟议方法(名为Directal GAN)的实用性,平均精度为86.4%,跨越不同的属性。