TY - JOUR
T1 - Information-based boundary equilibrium generative adversarial networks with interpretable representation learning
AU - Hah, Junghoon
AU - Lee, Woojin
AU - Lee, Jaewook
AU - Park, Saerom
N1 - Publisher Copyright:
Copyright © 2018 Junghoon Hah et al.
PY - 2018
Y1 - 2018
N2 - This paper describes a new image generation algorithm based on generative adversarial network. With an information-theoretic extension to the autoencoder-based discriminator, this new algorithm is able to learn interpretable representations from the input images. Our model not only adversarially minimizes the Wasserstein distance-based losses of the discriminator and generator but also maximizes the mutual information between small subset of the latent variables and the observation. We also train our model with proportional control theory to keep the equilibrium between the discriminator and the generator balanced, and as a result, our generative adversarial network can mitigate the convergence problem. Through the experiments on real images, we validate our proposed method, which can manipulate the generated images as desired by controlling the latent codes of input variables. In addition, the visual qualities of produced images are effectively maintained, and the model can stably converge to the equilibrium. However, our model has a difficulty in learning disentangling factors because our model does not regularize the independence between the interpretable factors. Therefore, in the future, we will develop a generative model that can learn disentangling factors.
AB - This paper describes a new image generation algorithm based on generative adversarial network. With an information-theoretic extension to the autoencoder-based discriminator, this new algorithm is able to learn interpretable representations from the input images. Our model not only adversarially minimizes the Wasserstein distance-based losses of the discriminator and generator but also maximizes the mutual information between small subset of the latent variables and the observation. We also train our model with proportional control theory to keep the equilibrium between the discriminator and the generator balanced, and as a result, our generative adversarial network can mitigate the convergence problem. Through the experiments on real images, we validate our proposed method, which can manipulate the generated images as desired by controlling the latent codes of input variables. In addition, the visual qualities of produced images are effectively maintained, and the model can stably converge to the equilibrium. However, our model has a difficulty in learning disentangling factors because our model does not regularize the independence between the interpretable factors. Therefore, in the future, we will develop a generative model that can learn disentangling factors.
UR - http://www.scopus.com/inward/record.url?scp=85056357026&partnerID=8YFLogxK
U2 - 10.1155/2018/6465949
DO - 10.1155/2018/6465949
M3 - Article
C2 - 30416519
AN - SCOPUS:85056357026
SN - 1687-5265
VL - 2018
JO - Computational Intelligence and Neuroscience
JF - Computational Intelligence and Neuroscience
M1 - 6465949
ER -