TextCycleGAN FY21 Technical Report

Abstract

In this report, we discuss progression during the final year of our effort on TextCycleGAN (TCG): a cycle-consistent generative adversarial network (CycleGAN) for image captioning. Fundamentally, TCG is optimizing separate Generative Adversarial Networks (GANs) to learn dual mappings between imagery and captions. By learning both functions constrained on a cycle-consistency loss, each individual mapping can become stronger. Throughout the effort, the team has faced challenges specific to GANs during development. This includes issues with gradient optimization and balance between generator and discriminator learning. We will review in detail our adjustments to TCG from previous years, these roadblocks we faced along the way, final status of the effort, and potential mitigation strategies when tackling this problem again in the future.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2022
Accession Number
AD1174619

Entities

People

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

Organizations

  • Naval Information Warfare Center Pacific

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Computer Languages
  • Computer Vision
  • Computers
  • Consistency
  • Discriminators
  • Generators
  • Information Warfare
  • Language
  • Military Research
  • Natural Language Processing
  • Natural Languages
  • Neural Networks
  • United States
  • United States Government

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design
  • Technical Research and Report Writing.