论文标题
Avae：对抗性变异自动编码器
AVAE: Adversarial Variational Auto Encoder
论文作者
论文摘要
在各种各样的图像生成模型中，两个模型脱颖而出：变分自动编码器（VAE）和生成对抗网络（GAN）。甘恩可以产生逼真的图像，但它们遭受模式崩溃的困扰，并且没有提供简单的方法来获得图像的潜在表示。另一方面，VAE没有这些问题，但是它们通常产生的图像不如甘恩。在本文中，我们解释说，这种缺乏现实主义部分是由于对自然图像歧管维度的共同低估。为了解决这个问题，我们引入了一个新的框架，该框架以一种新颖的和互补的方式将VAE和GAN结合起来，以产生自动编码模型，该模型在生成gan质量图像的同时保持VAES属性。我们在五个图像数据集上进行定性和定量评估我们的方法。
Among the wide variety of image generative models, two models stand out: Variational Auto Encoders (VAE) and Generative Adversarial Networks (GAN). GANs can produce realistic images, but they suffer from mode collapse and do not provide simple ways to get the latent representation of an image. On the other hand, VAEs do not have these problems, but they often generate images less realistic than GANs. In this article, we explain that this lack of realism is partially due to a common underestimation of the natural image manifold dimensionality. To solve this issue we introduce a new framework that combines VAE and GAN in a novel and complementary way to produce an auto-encoding model that keeps VAEs properties while generating images of GAN-quality. We evaluate our approach both qualitatively and quantitatively on five image datasets.
