TextCycleGan FY20

Abstract

In this report, we discuss improvements to TextCycleGAN: a cycle-consistent generative adversarial network (CycleGAN) for image captioning. CycleGANs train separate Generative Adversarial Networks (GANs) to learn mappings between multiple domains and strengthens each individual mapping with cycle consistency loss. As such, with CycleGANs we can create a better image captioning generator by jointly training an image synthesis generator. Since cycle-consistency ensures minimal change with recreation of the input, this offers a unique challenge for image captioning due to the many-to-many nature of the mapping from images to captions and vice-versa. We will further discuss how we tackle this many-to-many challenge as well as both image captioning and image synthesis in the report.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 01, 2022
Accession Number
AD1168651

Entities

People

  • Anthony C. Delgado
  • Antonius F. Panggabean
  • Mitch C. Manzanares
  • Mohammad R. Alam
  • Nicole A. Isoda

Organizations

  • Naval Undersea Warfare Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Automated Text Summarization
  • Computer Languages
  • Computer Vision
  • Consistency
  • Generators
  • Governments
  • Information Operations
  • Information Warfare
  • Language
  • Military Research
  • Natural Language Processing
  • Natural Languages
  • Probability
  • Probability Distributions
  • Training
  • United States
  • United States Government

Fields of Study

  • Computer science

Readers

  • Enterprise Information Systems Architecture and Joint Command Capability Interoperability Support.
  • Neural Network Machine Learning.