For bidirectional joint image-text modeling, we develop variational
hetero-encoder (VHE) randomized generative adversarial network (GAN), a
versatile deep generative model that integrates a probabilistic text decoder,
probabilistic image encoder, and GAN into a coherent end-to-end multi-modality
learning framework. VHE randomized GAN (VHE-GAN) encodes an image to decode its
associated text, and feeds the variational posterior as the source of
randomness into the GAN image generator. We plug three off-the-shelf modules,
including a deep topic model, a ladder-structured image encoder, and
StackGAN++, into VHE-GAN, which already achieves competitive performance. This
further motivates the development of VHE-raster-scan-GAN that generates
photo-realistic images in not only a multi-scale low-to-high-resolution manner,
but also a hierarchical-semantic coarse-to-fine fashion. By capturing and
relating hierarchical semantic and visual concepts with end-to-end training,
VHE-raster-scan-GAN achieves state-of-the-art performance in a wide variety of
image-text multi-modality learning and generation tasks.

本文提出了可变异率异质编码器随机生成对抗网络 (VHE-GAN)，并将其应用于双向联合图像文本建模，以生成更准确的图像及关联文本，同时通过对抗训练和可变后验进行端到端训练来捕捉和关联层次语义和视觉概念，从而在各类图片和文本生成任务中取得了最先进的性能。