  Variational auto-encoder (VAE) is a powerful unsupervised learning framework
for image generation. One drawback of VAE is that it generates blurry images
due to its Gaussianity assumption and thus L2 loss. To allow the generation of
high quality images by VAE, we increase the capacity of decoder network by
employing residual blocks and skip connections, which also enable efficient
optimization. To overcome the limitation of L2 loss, we propose to generate
images in a multi-stage manner from coarse to fine. In the simplest case, the
proposed multi-stage VAE divides the decoder into two components in which the
second component generates refined images based on the course images generated
by the first component. Since the second component is independent of the VAE
model, it can employ other loss functions beyond the L2 loss and different
model architectures. The proposed framework can be easily generalized to
contain more than two components. Experiment results on the MNIST and CelebA
datasets demonstrate that the proposed multi-stage VAE can generate sharper
images as compared to those from the original VAE.
