Joint generation of image and text with GANs
Joint generation of image and text with generative adversarial networks
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
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The computer vision and natural language processing communities have come together on image captioning related problems, but the fields have remained largely disjoint. There has been work in transforming text to text, images to text, text to images, and images to images, and work on generating images from nothing, and text from nothing, but no work on generating images and text together. This work looks at using GAN methods in order to generate images and text simultaneously using a shared representation. Visual representations of text are employed to use GAN techniques for both images and text. We present a framework for jointly generating images and text with a similarity loss that allows the model to learn a semantic representation.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 59-62).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.