论文标题
时尚风格的生成：潜在空间中使用高斯混合模型的进化搜索
Fashion Style Generation: Evolutionary Search with Gaussian Mixture Models in the Latent Space
论文作者
论文摘要
本文提出了一种新的方法，用于指导在FashionGen数据集中训练的生成对抗网络，以生成与目标时尚风格相对应的设计。在发电机的潜在空间中查找与样式相对应的潜在向量被作为进化搜索问题。使用高斯混合模型来基于服装特定属性预测模型中服装的高层表示识别时尚样式。一代世代以来，一种遗传算法优化了设计种群，以增加其属于高斯混合物组件或样式之一的可能性。表明开发的系统可以生成最大健身的图像，以视觉上的方式类似于某些样式，我们的方法提供了一个有希望的方向，可以指导搜索样式连接设计的设计。
This paper presents a novel approach for guiding a Generative Adversarial Network trained on the FashionGen dataset to generate designs corresponding to target fashion styles. Finding the latent vectors in the generator's latent space that correspond to a style is approached as an evolutionary search problem. A Gaussian mixture model is applied to identify fashion styles based on the higher-layer representations of outfits in a clothing-specific attribute prediction model. Over generations, a genetic algorithm optimizes a population of designs to increase their probability of belonging to one of the Gaussian mixture components or styles. Showing that the developed system can generate images of maximum fitness visually resembling certain styles, our approach provides a promising direction to guide the search for style-coherent designs.
