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InfoVAEGAN: Learning Joint Interpretable Representations by Information Maximization and Maximum Likelihood
2021
2021 IEEE International Conference on Image Processing (ICIP)
Learning disentangled and interpretable representations is an important step towards accomplishing comprehensive data representations on the manifold. In this paper, we propose a novel representation learning algorithm which combines the inference abilities of Variational Autoencoders (VAE) with the generalization capability of Generative Adversarial Networks (GAN). The proposed model, called InfoVAE-GAN, consists of three networks : Encoder, Generator and Discriminator. InfoVAEGAN aims to
doi:10.1109/icip42928.2021.9506169
fatcat:vupm743hencgnnbyf2t5gpydnm