Generative Moment Matching Autoencoder with Perceptual Loss

  • Mohammad Ahangar Kiasari
  • Dennis Singh Moirangthem
  • Minho LeeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)


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.


Generative Networks Moment Matching Autoencoder Convolutional Neural Networks Feature extraction 



This work was supported by the Industrial Strategic Technology Development Program (10044009) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea) (50%).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mohammad Ahangar Kiasari
    • 1
  • Dennis Singh Moirangthem
    • 1
  • Minho Lee
    • 1
    Email author
  1. 1.School of Electronics EngineeringKyungpook National UniversityDaeguSouth Korea

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