论文标题
使用生成变分的自动编码器生成高分辨率图像
Generate High Resolution Images With Generative Variational Autoencoder
论文作者
论文摘要
在这项工作中，我们提出了一个新型的神经网络，以产生高分辨率图像。我们在使用编码器时用鉴别器替换VAE的解码器。在发电机从高斯分布中喂养发电机时，编码器是从正常分布中馈出的。两者的组合都给出了一个歧视器，该歧视器告诉生成的图像是否正确。我们在3个不同的数据集上评估我们的网络：MNIST，LSUN和CEELBA数据集。我们的网络使用MMD，SSIM，日志可能性，重建错误，ELBO和KL Divergence作为评估指标，在产生大量更清晰的图像的同时，使用MMD，SSIM，日志可能性，重建错误，ELBO和KL Divergence击败了先前的艺术状态。这项工作可能非常令人兴奋，因为我们能够以原则上的贝叶斯方式结合生成模型和推理模型的优势。
In this work, we present a novel neural network to generate high resolution images. We replace the decoder of VAE with a discriminator while using the encoder as it is. The encoder is fed data from a normal distribution while the generator is fed from a gaussian distribution. The combination from both is given to a discriminator which tells whether the generated image is correct or not. We evaluate our network on 3 different datasets: MNIST, LSUN and CelebA dataset. Our network beats the previous state of the art using MMD, SSIM, log likelihood, reconstruction error, ELBO and KL divergence as the evaluation metrics while generating much sharper images. This work is potentially very exciting as we are able to combine the advantages of generative models and inference models in a principled bayesian manner.
