  A semi-recurrent hybrid VAE-GAN model for generating sequential data is
introduced. In order to consider the spatial correlation of the data in each
frame of the generated sequence, CNNs are utilized in the encoder, generator,
and discriminator. The subsequent frames are sampled from the latent
distributions obtained by encoding the previous frames. As a result, the
dependencies between the frames are maintained. Two testing frameworks for
synthesizing a sequence with any number of frames are also proposed. The
promising experimental results on piano music generation indicates the
potential of the proposed framework in modeling other sequential data such as
video.
