论文标题
自动编码生成对抗网络
Autoencoding Generative Adversarial Networks
论文作者
论文摘要
自Goodfellow等人以来的几年中。引入了生成的对抗网络（GAN），生成模型应用的广度和质量爆炸了。尽管这项工作，甘斯在看到主流采用之前还有很长的路要走，这在很大程度上是由于他们臭名昭著的训练不稳定。在这里，我提出了一种四个网络模型的自动编码生成对抗网络（AEGAN），该模型通过将对抗性损失和重建损失应用于生成的图像和生成的潜在矢量，从而在指定的潜在空间和给定的样本空间之间学习了肉体映射。 Aegan技术对典型的GAN训练提供了几种改进，包括训练稳定，预防模式崩溃，并允许真实样品之间的直接插值。使用动漫面部数据集说明了该技术的有效性。
In the years since Goodfellow et al. introduced Generative Adversarial Networks (GANs), there has been an explosion in the breadth and quality of generative model applications. Despite this work, GANs still have a long way to go before they see mainstream adoption, owing largely to their infamous training instability. Here I propose the Autoencoding Generative Adversarial Network (AEGAN), a four-network model which learns a bijective mapping between a specified latent space and a given sample space by applying an adversarial loss and a reconstruction loss to both the generated images and the generated latent vectors. The AEGAN technique offers several improvements to typical GAN training, including training stabilization, mode-collapse prevention, and permitting the direct interpolation between real samples. The effectiveness of the technique is illustrated using an anime face dataset.
