Joint generation of image and text with GANs
Name
1128823720-MIT.pdf
Size
12.3 MB
Format
Adobe PDF
Checksum (MD5)
69b9acf6677699295212b4250b855394
Author(s)
Shimanuki, Brian.
Advisor(s)
Antonio Torralba.
Alternative Title
Joint generation of image and text with generative adversarial networks
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 59-62).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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