论文标题
发电机知道歧视者在无条件的gan中应该学到什么
Generator Knows What Discriminator Should Learn in Unconditional GANs
论文作者
论文摘要
有条件图像产生的最新方法受益于密集的监督，例如分割标签图，以实现高保真性。但是，很少探索使用密集的监督进行无条件的图像产生。在这里，我们探讨了密集监督在无条件生成中的功效，找到生成器特征图可以是成本昂贵的语义标签图的替代方法。从我们的经验证据来看，我们提出了一个新的生成器引导的歧视官正则化（GGDR），其中生成器的特征映射监督了歧视者在无条件生成中具有丰富的语义表示。具体而言，我们采用了一个U-NET体系结构来进行鉴别器，该体系结构经过训练，可以预测发电机特征图作为输入的伪造图像。关于Mulitple数据集的广泛实验表明，我们的GGDR始终在定量和定性方面提高基线方法的性能。代码可从https://github.com/naver-ai/ggdr获得
Recent methods for conditional image generation benefit from dense supervision such as segmentation label maps to achieve high-fidelity. However, it is rarely explored to employ dense supervision for unconditional image generation. Here we explore the efficacy of dense supervision in unconditional generation and find generator feature maps can be an alternative of cost-expensive semantic label maps. From our empirical evidences, we propose a new generator-guided discriminator regularization(GGDR) in which the generator feature maps supervise the discriminator to have rich semantic representations in unconditional generation. In specific, we employ an U-Net architecture for discriminator, which is trained to predict the generator feature maps given fake images as inputs. Extensive experiments on mulitple datasets show that our GGDR consistently improves the performance of baseline methods in terms of quantitative and qualitative aspects. Code is available at https://github.com/naver-ai/GGDR
