Generative Moment Matching Autoencoder with Perceptual Loss

Abstract

In deep generative networks, one of the major challenges is to generate non-blurry, clearer images. Unlike the generative adversarial networks, generative models such as variational autoencoders, generative moment matching networks etc. use pixel-wise loss which leads to the generation of blurry images. In this paper, we propose an improved generative model called Generative Moment Matching Autoencoder (GMMA) with a feature-wise loss mechanism. We use a pre-trained VGGNet convolutional neural network to compute the loss at the various feature extraction layers. We evaluate the performance of our model on the MNIST and the Large-scale CelebFaces Attributes (CelebA) dataset. Our generative model outperforms the existing models on the log-likelihood estimation test. We also illustrate the effectiveness of our mechanism and the improved generation and reconstruction capabilities. The proposed GMMA with perceptual loss successfully alleviates the problem of blurry image generation.