论文标题
gan的发电机之间的共同损失
Shared Loss between Generators of GANs
论文作者
论文摘要
生成对手网络是能够以高精度复制输入数据的隐式概率分布的生成模型。传统上，gan由一个发电机和歧视器组成，它们相互相互作用以产生高度现实的人工数据。传统的gans是模式崩溃问题的猎物，这意味着它们无法生成输入数据集中存在的数据的不同变化。最近，多个发电机已通过减轻模式崩溃问题来产生更现实的输出。我们使用此多个生成器框架。本文的新颖性在于使发电机同时与歧视者互动时相互竞争。我们表明，这会导致gan的训练时间急剧减少，而不会影响其性能。
Generative adversarial networks are generative models that are capable of replicating the implicit probability distribution of the input data with high accuracy. Traditionally, GANs consist of a Generator and a Discriminator which interact with each other to produce highly realistic artificial data. Traditional GANs fall prey to the mode collapse problem, which means that they are unable to generate the different variations of data present in the input dataset. Recently, multiple generators have been used to produce more realistic output by mitigating the mode collapse problem. We use this multiple generator framework. The novelty in this paper lies in making the generators compete against each other while interacting with the discriminator simultaneously. We show that this causes a dramatic reduction in the training time for GANs without affecting its performance.
