论文标题
超分辨率变异自动编码器
Super-resolution Variational Auto-Encoders
论文作者
论文摘要
变分自动编码器（VAE）的框架提供了一种原则性的方法，用于共同学习潜在的可变量模型和相应的推理模型。但是，这种方法的主要缺点是生成图像的模糊。一些研究将此效果与目标函数联系起来，即（负）对数可能性。在这里，我们建议通过添加一个随机变量来增强VAE，该变量是原始图像的缩小版本，并且仍然使用日志样式函数作为学习目标。此外，通过提供缩小的图像作为解码器的输入，它可以以类似于超分辨率的方式使用。我们从经验上介绍了所提出的方法在负模样中与VAE相当，但在数据合成中获得了更好的FID得分。
The framework of variational autoencoders (VAEs) provides a principled method for jointly learning latent-variable models and corresponding inference models. However, the main drawback of this approach is the blurriness of the generated images. Some studies link this effect to the objective function, namely, the (negative) log-likelihood. Here, we propose to enhance VAEs by adding a random variable that is a downscaled version of the original image and still use the log-likelihood function as the learning objective. Further, by providing the downscaled image as an input to the decoder, it can be used in a manner similar to the super-resolution. We present empirically that the proposed approach performs comparably to VAEs in terms of the negative log-likelihood, but it obtains a better FID score in data synthesis.
