Generative Adversarial Networks (GAN) is currently widely used as an unsupervised image generation method. Current state-of-the-art GANs can generate photorealistic images with high resolution. However, a large amount of data is required, or the model would prone to generate images with similar patterns (mode collapse) and bad quality. I proposed an additional structure and loss function for GANs called LFM, trained to maximize the feature diversity between the different dimensions of the latent space to avoid mode collapse without affecting the image quality. Orthogonal latent vector pairs are created, and feature vector pairs extracted by discriminator are examined by dot product, with which discriminator and generator are in a novel adversarial relationship. In experiments, this system has been built upon DCGAN and proved to have improvement on Frechet Inception Distance (FID) training from scratch on CelebA Dataset. This system requires mild extra performance and can work with data augmentation methods. The code is available on github.com/penway/LFM.
翻译:目前,最先进的GANs能够产生高分辨率的光现实图像。然而,需要大量数据,或者模型容易生成类似模式(模式崩溃)和质量差的图像。我提议为GANs增加一个称为LFM的结构和损失功能,该功能旨在尽可能扩大潜在空间不同维度的特征多样性,以避免模式崩溃,同时不影响图像质量。创建了正方形潜载矢量配方,由歧视者提取的特质矢量配方通过点产品进行检查,而歧视者和生成者则处于一种新的对抗关系中。在实验中,这个系统建在DCGAN上,并证明在CelibA数据集的Frechet Inpeption距离(FID)培训方面有改进。这个系统需要微量的额外性能,并且可以使用数据增强方法。代码可在 github.com/penway/LFMT上查阅。