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.
@cite_11: The goal of this paper is not to introduce a single algorithm or method, but to make theoretical steps towards fully understanding the training dynamics of generative adversarial networks. In order to substantiate our theoretical analysis, we perform targeted experiments to verify our assumptions, illustrate our claims, and quantify the phenomena. This paper is divided into three sections. The first section introduces the problem at hand. The second section is dedicated to studying and proving rigorously the problems including instability and saturation that arize when training generative adversarial networks. The third section examines a practical and theoretically grounded direction towards solving these problems, while introducing new tools to study them.
Instead of adversarial training widely used in previous methods, the generative model of our proposed SDN has a feedforward structure and learns under the guidance of generic discriminative model. With this paradigm, SDN does not need minimax optimization and careful parameter adjustment of two adversarial models, which is more stable than contemporary GAN based methods @cite_11 .