论文标题
Deshufflegan：一个自制的gan，以改善结构学习
DeshuffleGAN: A Self-Supervised GAN to Improve Structure Learning
论文作者
论文摘要
生成对抗网络（GAN）引发了人们对图像生成问题的兴趣，因为它们提高了输出图像质量和多功能性，可扩展到新方法。许多基于GAN的作品试图通过基于建筑和损失的扩展来改善发电。我们认为，从现实主义和与原始数据分布相似的角度提高GAN性能的关键点之一是能够为模型提供学习数据中空间结构的能力。为此，我们建议Deshufflegan通过自学方法来增强歧视者和发电机的学习。具体来说，我们引入了一项脱颖而出的任务，该任务解决了一个随机洗牌的图像瓷砖的难题，这反过来又有助于DeShufflegan学会增加其空间结构和现实外观的表达能力。与基线方法相比，我们为生成图像的性能提高提供了实验证据，这在两个不同的数据集上始终观察到。
Generative Adversarial Networks (GANs) triggered an increased interest in problem of image generation due to their improved output image quality and versatility for expansion towards new methods. Numerous GAN-based works attempt to improve generation by architectural and loss-based extensions. We argue that one of the crucial points to improve the GAN performance in terms of realism and similarity to the original data distribution is to be able to provide the model with a capability to learn the spatial structure in data. To that end, we propose the DeshuffleGAN to enhance the learning of the discriminator and the generator, via a self-supervision approach. Specifically, we introduce a deshuffling task that solves a puzzle of randomly shuffled image tiles, which in turn helps the DeshuffleGAN learn to increase its expressive capacity for spatial structure and realistic appearance. We provide experimental evidence for the performance improvement in generated images, compared to the baseline methods, which is consistently observed over two different datasets.
