Information - Theoretical Transfer Learning for Bridging the Gap Between Simulated and Real-World Data

Abstract

During this one-year STIR project, the team has contributed two fundamental machine learning algorithms in image-to-image translation and data fusion, as well as collecting both simulated and real world datasets, which are of great significance in autonomous driving.

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Document Details

Document Type
Technical Report
Publication Date
Nov 03, 2022
Accession Number
AD1212606

Entities

People

  • Bo Tang

Organizations

  • Mississippi State University

Tags

Communities of Interest

  • Autonomy
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Ablation
  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Autonomous Vehicles
  • Boundaries
  • Computer Vision
  • Data Fusion
  • Data Processing
  • Data Sets
  • Disparities
  • Image Processing
  • Interpolation
  • Learning
  • Machine Learning
  • Mississippi
  • Models
  • Sensor Fusion
  • Simulations
  • Simulators
  • United States
  • Vehicles

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Research Science/Academic Research
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks