Lifelong Visual Episodic Memory

Abstract

This report describes work on Lifelong Learning that develops methods for using Generative Adversarial Networks (GANs) to represent the probability of images. This problem is integral to developing the ability to detect and characterize domain shift. It also explores causality in video. Causal reasoning may play a key role in the ability of an agent to adapt to changing environments. If one understands what components of the environment lead to a particular outcome, one can determine whether changes to the environment affect these causal factors and should affect expected outcomes. It also explores semantic segmentation in the presence of domain shift.

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Document Details

Document Type
Technical Report
Publication Date
Sep 15, 2020
Accession Number
AD1108933

Entities

People

  • David R. Jacobs
  • Ronen Basri
  • Tom Goldstein

Organizations

  • University of Maryland
  • Weizmann Institute of Science

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Artificial Neural Networks
  • Automata Theory
  • Climate Change
  • Computer Science
  • Computer Vision
  • Computers
  • Dimensionality Reduction
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Monte Carlo Method
  • Network Science
  • Neural Networks
  • Pattern Recognition
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Reasoning
  • Reinforcement Learning

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design