Review — EBGAN: Energy-Based Generative Adversarial Network (GAN)



Original Source Here

Review — EBGAN: Energy-Based Generative Adversarial Network (GAN)

Using Autoencoder at Discriminator, Using Repelling Regularizer at Generator

EBGAN: low energies to the regions near the data manifold and higher energies to other regions. (Figure from https://www.slideshare.net/MingukKang/ebgan)

In this story, Energy-based Generative Adversarial Network, (EBGAN), by New York University, and Facebook Artificial Intelligence Research, is briefly reviewed. In this paper:

  • EBGAN views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions.
  • Similar to the probabilistic GANs, the generator is seen as being trained to produce contrastive samples with minimal energies, while the discriminator is trained to assign high energies to these generated samples.

This is a paper in 2017 ICLR with over 1000 citations. (Sik-Ho Tsang @ Medium)

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