CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training

@article{Bao2017CVAEGANFI,
  title={CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training},
  author={Jianmin Bao and Dong Chen and Fang Wen and Houqiang Li and Gang Hua},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={2764-2773}
}
We present variational generative adversarial networks, a general learning framework that combines a variational auto-encoder with a generative adversarial network, for synthesizing images in fine-grained categories, such as faces of a specific person or objects in a category. Our approach models an image as a composition of label and latent attributes in a probabilistic model. By varying the fine-grained category label fed into the resulting generative model, we can generate images in a… CONTINUE READING
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