条件约束下的自我注意生成对抗网络

为了解决生成对抗网络中因生成图像的特征信息表示不足而导致生成效果特征不明显、图像的关键特征信息模糊的问题, 提出了一种条件自我注意生成对抗网络的图像生成方法。该网络结合自我注意生成对抗网络的优点,向生成器和判别器中添加附加条件特征,明确指示模型生成对应的标志性类别信息,将数据的具体维度与语义特征关联起来,用这种方法提取其中的生成模型,使生成特定类型的图像的特征表示更加贴合原始数据分布。实验结果表明,所提出的方法在CelebA和MNIST数据集上的弗雷歇距离值相比较于自我注意生成对抗网络分别约提高了1.26和2.47。验证了所提出的方法相比较于其他的监督类生成模型可以有效地提升图像的质量效果以及多样性,并且可以有效地加快网络的收敛速度。

Self-attention generative adversarial network with the conditional constraint

In order to solve the problem that the feature information of the generated image against the network is insufficient, so that the generated effect characteristic is not obvious, and the key feature information of the image is blurred, this paper proposes an image generating method for a conditional self-attention generative adversarial network. The network combines the advantages of the self-attention generative adversarial network, and adds additional conditional features to the generator and the discriminator. It is explicitly indicated that the model can generate corresponding iconic category information. The specific dimensions of the data are related to the semantic features. In this way, the generation model is extracted, so that the feature representations of the images of a particular type are more closely matched to the original data distribution. Experimental results show that the FID values of the proposed method on the CelebA and MNIST data sets are increased by 1.26 and 2.47, respectively, compared with the self-attention generative confrontation network. It is verified that compared with other supervised class generation models, the proposed method can effectively improve the image quality and diversity, and can make the network converge faster.