Disconnected Manifold Learning for Generative Adversarial Networks
Disconnected Manifold Learning for Generative Adversarial Networks
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发表时间：
2018-06
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通讯作者：
Mahyar Khayatkhoei;Maneesh Kumar Singh;A. Elgammal
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作者：
Mahyar Khayatkhoei;Maneesh Kumar Singh;A. Elgammal
自然图像可能位于不相交流形的并集上，而不是一个全局连接的流形上，这可能会给常见的生成对抗网络（GAN）的训练带来一些困难。在这项工作中，我们首先证明了单生成器GAN无法正确地对断开流形上支持的分布进行建模，并研究了样本质量、模式丢弃和局部收敛如何受到影响。接下来，我们将展示如何使用一组生成器来解决这个问题，为这种多生成器GAN的成功提供新的见解。最后，我们解释了所造成的严重问题，考虑一个固定的先验以上的发电机的集合，并提出了一种新的方法来学习的先验和推断所需的发电机数量没有任何监督。我们提出的修改可以应用于任何其他GAN模型之上，以实现对不连通流形上支持的分布的学习。我们进行了几个实验来说明GAN的上述缺点，其在实践中的后果，以及我们提出的修改在缓解这些问题方面的有效性。
Natural images may lie on a union of disjoint manifolds rather than one globally connected manifold, and this can cause several difficulties for the training of common Generative Adversarial Networks (GANs). In this work, we first show that single generator GANs are unable to correctly model a distribution supported on a disconnected manifold, and investigate how sample quality, mode dropping and local convergence are affected by this. Next, we show how using a collection of generators can address this problem, providing new insights into the success of such multi-generator GANs. Finally, we explain the serious issues caused by considering a fixed prior over the collection of generators and propose a novel approach for learning the prior and inferring the necessary number of generators without any supervision. Our proposed modifications can be applied on top of any other GAN model to enable learning of distributions supported on disconnected manifolds. We conduct several experiments to illustrate the aforementioned shortcoming of GANs, its consequences in practice, and the effectiveness of our proposed modifications in alleviating these issues.