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Poster
IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis
Huaibo Huang · zhihang li · Ran He · Zhenan Sun · Tieniu Tan

Tue Dec 04 07:45 AM -- 09:45 AM (PST) @ Room 210 #13
in Tue Poster Session A »

We present a novel introspective variational autoencoder (IntroVAE) model for synthesizing high-resolution photographic images. IntroVAE is capable of self-evaluating the quality of its generated samples and improving itself accordingly. Its inference and generator models are jointly trained in an introspective way. On one hand, the generator is required to reconstruct the input images from the noisy outputs of the inference model as normal VAEs. On the other hand, the inference model is encouraged to classify between the generated and real samples while the generator tries to fool it as GANs. These two famous generative frameworks are integrated in a simple yet efficient single-stream architecture that can be trained in a single stage. IntroVAE preserves the advantages of VAEs, such as stable training and nice latent manifold. Unlike most other hybrid models of VAEs and GANs, IntroVAE requires no extra discriminators, because the inference model itself serves as a discriminator to distinguish between the generated and real samples. Experiments demonstrate that our method produces high-resolution photo-realistic images (e.g., CELEBA images at (1024^{2})), which are comparable to or better than the state-of-the-art GANs.

Keywords: Variational Inference · Adversarial Networks · Generative Models
[ Paper] 

Author Information

Huaibo Huang (Institute of Automation, Chinese Academy of Science)
zhihang li (Institute of Automation, Chinese Academy of Science)
Ran He (NLPR, CASIA)
Zhenan Sun (Institute of Automation, Chinese Academy of Sciences (CASIA))
Tieniu Tan (Chinese Academy of Sciences)

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