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Improving Discriminator-Generator Balance in Generative Adversarial Networks
Nov 07, 2017 (modified: Nov 07, 2017)ICLR 2018 Conference Blind Submissionreaders: everyoneShow Bibtex
Abstract:Training Artificial Neural Networks to generate better images is a hard problem
to solve with supervised techniques. Generative Adversarial Networks (GAN) is
an unsupervised approach that utilizes two ANNs in one system. The first neural
network is called the Discriminator and evaluates the quality of images generated
by the other network, the Generator. However, image generation has no fixed
solution, making evaluation difficult. This can be addressed through comparing
two GAN models by letting them evaluate each other; the paper proposes a custom
implementation of this evaluation method that allows for comparison between two
GAN models both overall and throughout the learning process.
GANs have a reputation for being hard to train. One concrete problem is main-
taining the balance between the Generator and Discriminator. As for humans, it
is easier to rate the quality of images then it is to actually create them. A good
evaluator is necessary, but it must not out-power the generative model. The paper
explores two main approaches to achieve a better balanced GAN model. The first
method makes guided alterations to the usually random input of the Generator.
The second method adds an additional Discriminator to the model. Techniques
based on both of these methods were shown to effectively guide the training pro-
cess and creating strong models that outperformed regular GANs when compared
using the previously mentioned evaluation method.
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