  Variational Convertor-Encoder (VCE) converts an image to various styles; we
present this novel architecture for the problem of one-shot generalization and
its transfer to new tasks not seen before without additional training. We also
improve the performance of variational auto-encoder (VAE) to filter those
blurred points using a novel algorithm proposed by us, namely large margin VAE
(LMVAE). Two samples with the same property are input to the encoder, and then
a convertor is required to processes one of them from the noisy outputs of the
encoder; finally, the noise represents a variety of transformation rules and is
used to convert new images. The algorithm that combines and improves the
condition variational auto-encoder (CVAE) and introspective VAE, we propose
this new framework aim to transform graphics instead of generating them; it is
used for the one-shot generative process. No sequential inference algorithmic
is needed in training. Compared to recent Omniglot datasets, the results show
that our model produces more realistic and diverse images.
