We study in this paper the problems of both image captioning and text-to-image generation, and present a novel turbo learning approach to jointly training an image-to-text generator (a.k.a.

Summary: This paper proposed a joint aproach for learning two network: a capitonbot that generates a caption given an image and a drawingbot that generates an image given a caption. For both caption and image generators, the authors use existing network architecture. LSTM - based network that incorporates an image feature produced by Resnet is used for caption generation (the specific architecture is not clearly described). Attention GAN is used to generate an image from caption. The main contribution of this paper is joint training of caption and image generators by constructing two auto-encoders. An image auto-encoder consists of a caption generator feeding an image generator.