Our GAN model, named as SyncGAN, can successfully generate synchronous data (e.g., a pair of image and sound) from identical random noise. For transforming data�...
ABSTRACT. Generative adversarial network (GAN) has achieved impres- sive success on cross-domain generation, but it faces diffi- culty in cross-modal�...
Our GAN model, named as SyncGAN, can successfully generate synchronous data (e.g., a pair of image and sound) from identical random noise. For transforming data�...
A novel network component named synchronizer is proposed in this work to judge whether the paired data is synchronous/corresponding or not, which can constrain�...
Syncgan: Synchronize the latent spaces of cross-modal generative adversarial networks. MaoX. et al. Semantic invariant cross-domain image generation with�...
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Mar 15, 2019 � Abstract. Generative Adversarial Networks (GANs) are shown to be successful at generating new and realistic samples including 3D object.
SyncGAN - SyncGAN: Synchronize the Latent Space of Cross-modal Generative Adversarial Networks; S^2GAN - Generative Image Modeling using Style and Structure�...
SyncGAN: Synchronize the Latent Space of Cross-modal Generative Adversarial Networks � Wen-Cheng ChenChien-Wen ChenMin-Chun Hu. Computer Science. arXiv.org.
In this paper, we propose a novel cross-modal varia- tional alignment method in order to process and relate in- formation across different modalities.
Missing: Syncgan: | Show results with:Syncgan:
Generative Adversarial Networks (GANs) are shown to be successful at generating new and realistic samples including 3D object models. Conditional GAN, a�...