基于生成对抗网络的无监督图像风格迁移

无监督的图像风格迁移是计算机视觉领域中一个非常重要且具有挑战性的问题.无监督的图像风格迁移旨在通过给定类的图像映射到其他类的类似图像.一般情况下成对匹配的数据集很难获得,这极大限制了图像风格迁移的转换模型.因此,为了避免这种限制,对现有的无监督的图像风格迁移的方法进行改进,采用改进的循环一致性对抗网络进行无监督图像风格迁移.首先为了提升网络的训练速度,避免梯度消失的现象出现,在传统的循环一致性网络生成器部分引入DenseNet网络;在提高生成器的性能方面,生成器网络部分引入attention机制来输出效果更好的图像;为了减少网络的结构风险,在网络的每一个卷积层都使用谱归一化.为了验证本文方法的有效性,在monet2photo、vangogh2photo和facades数据集上进行了实验,实验结果表明,该方法在Inception score平均分数和FID距离评价指标上均有所提高.

Unsupervised image style transfer based on generating adversarial network

Unsupervised transfer of image style is a very important and challenging problem in the field of computer vision. Unsupervised image style migration is intended to map images of a given class to similar images of other classes. In general, pairwise matching data sets are difficult to obtain, which greatly limits the transformation model of image style migration. Therefore, in order to avoid this limitation, this paper improves the existing unsupervised image style transfer method and adopts an improved cycle consistency adversarial network to conduct unsupervised image style transfer. Firstly, in order to improve the training speed of the network and avoid the phenomenon of gradient disappearing, this paper introduces Densenet network into the traditional cycle consistent network generator. In terms of improving the performance of generators, the generator network introduces the attention mechanism to output better images. In order to reduce the structural risk of the network, spectral normalization is used in each convolutional layer of the network. In order to verify the effectiveness of the proposed method, experiments are carried out on the datasets of monet2photo, vangogh2photo, and facades, the experimental results show that the average of Inception score and FID distance evaluation index are improved.