  Text-to-image synthesis aims to automatically generate images according to
text descriptions given by users, which is a highly challenging task. The main
issues of text-to-image synthesis lie in two gaps: the heterogeneous and
homogeneous gaps. The heterogeneous gap is between the high-level concepts of
text descriptions and the pixel-level contents of images, while the homogeneous
gap exists between synthetic image distributions and real image distributions.
For addressing these problems, we exploit the excellent capability of generic
discriminative models (e.g. VGG19), which can guide the training process of a
new generative model on multiple levels to bridge the two gaps. The high-level
representations can teach the generative model to extract necessary visual
information from text descriptions, which can bridge the heterogeneous gap. The
mid-level and low-level representations can lead it to learn structures and
details of images respectively, which relieves the homogeneous gap. Therefore,
we propose Symmetrical Distillation Networks (SDN) composed of a source
discriminative model as "teacher" and a target generative model as "student".
The target generative model has a symmetrical structure with the source
discriminative model, in order to transfer hierarchical knowledge accessibly.
Moreover, we decompose the training process into two stages with different
distillation paradigms for promoting the performance of the target generative
model. Experiments on two widely-used datasets are conducted to verify the
effectiveness of our proposed SDN.
