IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis


WHY?

VAE can learn useful representation while GAN can sample sharp images.

WHAT?

Introspective Variational Autoencoder(IVAE) combines the advantage of VAE and GAN to make a model to learn useful representation and output sharp images. IVAE uses encoder to introspectively estimate the generated samples and the training data as a discriminator. image

L_{AE} = -E_{q_{\phi}(z|x)}log p_{\theta}(x|z) = \frac{1}{2}\sum_{i=1}^N\|x_{r,i}-x_i\|^2_F\\
L_{REG} = D_{KL}(q_{\phi}(z|x)\|p(z)) = \frac{1}{2}\sum_{i=1}^N(1+log(\sigma_i^2) - \mu_i^2 - \sigma_i^2)\\
L_E(x,z) = E(x) + [m-E(G(z))]^+ + L_{AE}(x) = L_{REG}(Enc(x)) + \alpha\sum_{s=r,p}[m-L_{REG}(Enc(ng(x_s)))]^+ + \beta L_{AE}(x, x_r)\\
L_G(z) = E(G(z)) + L_{AE}(x) = \alpha \sum_{s=r,p} L_{REG}(Enc(x_s)) + \beta L_{AE}(x)

Algorithm is as follows.

image

So?

IVAE achieved realistic quality reconstruction and sample from CelebA, CelebA-HQ, and LSUN Bedroom. Also the representations learned from IVAE showed meaningful latent manifold.

Critic

Amazing image quality!

Huang, Huaibo, et al. “IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis.” arXiv preprint arXiv:1807.06358 (2018).



© 2017. by isme2n

Powered by aiden