| dc.description.abstract | With the rapid development of artificial intelligence, machine learning has achieved excellent results in image recognition, semantic recognition, image generation, etc. The deep meaning of the words “artificial intelligence” are the wisdom of human being. Let the computer to learn the way to get a certain logical judgment ability, which is what we have achieved at present, but if we look at the development of artificial intel-ligence from a microscopic point of view, we still have not reached the true intelligence.
This paper is mainly to simulate the imagination of human brain. In the field of text-to-image, there have been some researches, such as StackGAN, StackGAN++ and AttnGAN in recent years, but their initial goal is to target bird dataset (CUB-200) and flower (102Flowers) dataset for training and optimization. Usually when people imag-ine a thing, they usually give a description of the thing. The ultimate goal of this paper is to produce a narrative photo with description. In present stage, we make neural-based network an ability of generating scene photos corresponded to the description and en-hance the diversity with our dataset.
In order to make the generated images more diverse than a specific single image, this paper uses the hidden layer information of the image to initialize the RNN Memory Cell to produce a narrative photo. From the experimental results, it indeed works. Comparing to the original AttnGAN architecture, our proposed method does help to increase the diversity of generated images.
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