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A Comparative Study on Variational Autoencoders and Generative Adversarial Networks
Ist Teil von
2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT), 2019, p.1-5
Ort / Verlag
IEEE
Erscheinungsjahr
2019
Quelle
IEEE/IET Electronic Library (IEL)
Beschreibungen/Notizen
Generative Adversarial Networks (GAN) have been remarkable at generating artificial data, especially realistic looking images. This learning framework has proven itself to be effective in synthetic image generation, semantic image hole filling, semantic image editing, style transfer and many more. On the other hand, variational auto-encoders (VAE) have also been quite effective, so much so that mathematically it is often more accurate at generating images resembling to its original dataset. Nevertheless, images generated by VAE suffer from blurriness and are generally less realistic looking from human perception. In this paper we take a broad view on both systems and propose a theoretical approach to combine them and bring out the best of both.