论文标题
GLEAD：通过发电机领导任务改善gan
GLeaD: Improving GANs with A Generator-Leading Task
论文作者
论文摘要
生成对抗网络（GAN）是在发电机（G）和鉴别器（D）之间的两种玩家游戏的配制，在该游戏中要求D区分D图像是来自真实数据还是由G。在这样的配方下产生的图像，D在规则制造商中扮演D扮演的情况，因此倾向于统治竞争。为了在甘斯（Gans）进行更公平的游戏，我们提出了一个新的范式进行对抗训练，这也使G也将任务分配给D。具体而言，给定图像，我们希望D提取可以通过G充分解码的代表性特征来重建输入。这样一来，就敦促D与域分类的G视图保持一致，而不是自由学习。各种数据集的实验结果证明了我们的方法比基线的优势。例如，我们在LSUN卧室上将StyleGAN2的FID从4.30提高到2.55，在LSUN教堂上将4.04提高到2.82。我们认为，这项工作中提出的开创性尝试可以通过更好地设计的生成器领导的任务来激发社区的改进。
Generative adversarial network (GAN) is formulated as a two-player game between a generator (G) and a discriminator (D), where D is asked to differentiate whether an image comes from real data or is produced by G. Under such a formulation, D plays as the rule maker and hence tends to dominate the competition. Towards a fairer game in GANs, we propose a new paradigm for adversarial training, which makes G assign a task to D as well. Specifically, given an image, we expect D to extract representative features that can be adequately decoded by G to reconstruct the input. That way, instead of learning freely, D is urged to align with the view of G for domain classification. Experimental results on various datasets demonstrate the substantial superiority of our approach over the baselines. For instance, we improve the FID of StyleGAN2 from 4.30 to 2.55 on LSUN Bedroom and from 4.04 to 2.82 on LSUN Church. We believe that the pioneering attempt present in this work could inspire the community with better designed generator-leading tasks for GAN improvement.
