  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.
