Despite growing insights into the GAN training, it still suffers from instability during the training procedure. To alleviate this problem, this paper presents a novel convolutional layer, called perturbed-convolution (PConv), which focuses on achieving two goals simultaneously: penalize the discriminator for training GAN stably and prevent the overfitting problem in the discriminator. PConv generates perturbed features by randomly disturbing an input tensor before performing the convolution operation. This approach is simple but surprisingly effective. First, to reliably classify real and generated samples using the disturbed input tensor, the intermediate layers in the discriminator should learn features having a small local Lipschitz value. Second, due to the perturbed features in PConv, the discriminator is difficult to memorize the real images; this makes the discriminator avoid the overfitting problem. To show the generalization ability of the proposed method, we conducted extensive experiments with various loss functions and datasets including CIFAR-10, CelebA-HQ, LSUN, and tiny-ImageNet. Quantitative evaluations demonstrate that WCL significantly improves the performance of GAN and conditional GAN in terms of Frechet inception distance (FID). For instance, the proposed method improves FID scores on the tiny-ImageNet dataset from 58.59 to 50.42.
翻译:尽管对GAN培训的认识日益深入,但它在培训过程中仍然处于不稳定状态。为了缓解这一问题,本文件展示了一个新的革命层,称为突变革命(PConv),其重点是同时实现两个目标:对培训GAN的歧视问题进行惩罚,刺穿,并防止歧视者出现过重的问题。PConv通过随机干扰输入点来产生动荡特征,然后才进行革命行动。这一方法简单但令人惊讶地有效。首先,利用被破坏的输入点对真实和生成的样本进行可靠分类,歧视者中间层应当学习当地小利普西茨价值的特征。第二,由于PConv的渗透特征,歧视者难以同时记住真实图像;这使得歧视者避免了歧视者过分适应的问题。为了显示拟议方法的概括性,我们进行了广泛的实验,包括CIFAR-10、CeebeA-HQ、LSUN和微小IMageNet等各种损失功能和数据集。定量评估表明,WLLLCL会大大改进GAN-FID-58的运行情况,在FID-50级的远程模型上改进了GAN-ISM-Set-IAN的运行。