用于自然图像和艺术品条件合成的改进ArtGAN
Improved ArtGAN for Conditional Synthesis of Natural Image and Artwork
摘要：
本文提出了一系列新的方法来改进生成对抗网络（GAN）的条件图像合成，我们将所提出的模型命名为ArtGAN。ArtGAN的一个关键创新是，损失函数的梯度w.r.t.将标签（随机分配给每个生成的图像）从分类标签反向传播到生成器。通过标签信息的反馈，生成器能够更有效地学习并生成质量更好的图像。最近的作品的启发，一个自动编码器被纳入到额外的补充信息的分类编码。最后，我们引入了一种新的策略来提高图像质量。在实验中，我们通过消融研究在CIFAR-10和STL-10上评估ArtGAN。实证结果表明，我们提出的模型优于国家的最先进的结果CIFAR-10的Inception分数。我们证明了ArtGAN能够在Oxford-102和CUB-200上生成外观逼真的图像，并能够根据风格，艺术家和流派绘制逼真的艺术作品。源代码和模型可在以下网址获得：https://github.com/cs-chan/ArtGAN
英文摘要：
This paper proposes a series of new approaches to improve Generative Adversarial Network (GAN) for conditional image synthesis and we name the proposed model as ArtGAN. One of the key innovation of ArtGAN is that, the gradient of the loss function w.r.t. the label (randomly assigned to each generated image) is back-propagated from the categorical discriminator to the generator. With the feedback from the label information, the generator is able to learn more efficiently and generate image with better quality. Inspired by recent works, an autoencoder is incorporated into the categorical discriminator for additional complementary information. Last but not least, we introduce a novel strategy to improve the image quality. In the experiments, we evaluate ArtGAN on CIFAR-10 and STL-10 via ablation studies. The empirical results showed that our proposed model outperforms the state-of-the-art results on CIFAR-10 in terms of Inception score. Qualitatively, we demonstrate that ArtGAN is able to generate plausible-looking images on Oxford-102 and CUB-200, as well as able to draw realistic artworks based on style, artist, and genre. The source code and models are available at: https://github.com/cs-chan/ArtGAN
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
