强化组合式生成对抗网络

近年来通过在生成对抗网络中加入条件标签，控制生成图像的类别或属性已经取得很大进展，但是生成图像的类别或属性的准确性有待提高。为此，在生成对抗网络的判别器中加入了强化学习，通过上一次的分类结果去指导当前的分类。另外为了让生成的细粒度图像更加逼真，使用注意力机制在只增加少量的计算损失下让图像有全局感受野，将多属性的星型生成对抗网络与自注意力生成对抗网络组合后的生成图像的质量较高。 强化组合式生成对抗网络的最大均差达到0.036 93，最近邻指标效果较优，能自动化较准确地生成指定了某些属性的艺术图像，实验生成的图片也能用来解决缺乏数据的问题。

Reinforced-combined generative adversarial networks

In recent years, great progress has been made in controlling the categories or attributes of generated images by adding condition tags to the generation adversarial networks. However, the accuracy of the category or attribute of generated image needs to be improved. In order to solve this problem,we add reinforcement learning to the generator of generation adversarial networks, which guides the current classification by previous. In addition, the attention mechanism is used which makes a global sensory field to the images with only a small amount of computational loss. We combines multi-attribute star generation adversarial networks with self-attention generation adversarial networks which improves the quality of generated. maximum mean discrepancy reaches to 0.036 93 and the 1-nearest neighbor classifier has a batter effect by reinforced-combined generative adversarial networks, which can generate the art images that certain attributes are assigned automatically and accurately. The generated images can also be used to address the lack of data.