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- W3015118034 abstract "Recent GANs have achieved great progress in generating high quality images, however, GANs still suffer a lot from instability and mode collapse. In this work, we propose a new framework, Latent Optimization Generative Adversarial Networks (LO-GANs). We give a definition of mode collapse and derive that generator with capability of reconstructing training images alleviates the mode collapse. Moreover, this capability benefits the stability of GANs. Our method takes advantages of both GANs and non-adversarial methods, as it is capable of both reconstructing training images and generating new high-quality images. Instead of learning a images representation by employing an encoder, we directly optimize a learnable image representation set for image reconstruction. Besides image reconstruction, adversarial regularization is adopted to match the aggregated distribution of the latent image representation with arbitrary prior distribution. Our framework is flexible as it is implemented by adding a latent image representation set and a representation discriminator to vanilla GANs. Experimental results show that our models are capable of reconstructing training images and mode collapse is alleviated significantly. Benefitting from reconstructing constraints and latent image representation optimization, our models are more stable during training than vanilla GANs." @default.
- W3015118034 created "2020-04-10" @default.
- W3015118034 creator A5019708391 @default.
- W3015118034 creator A5068799172 @default.
- W3015118034 date "2019-12-01" @default.
- W3015118034 modified "2023-09-28" @default.
- W3015118034 title "Optimizing Generative Adversarial Networks in Latent Space" @default.
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- W3015118034 doi "https://doi.org/10.1109/icicas48597.2019.00020" @default.
- W3015118034 hasPublicationYear "2019" @default.
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