Using the Representation in a Neural Network's Hidden Layer for Task-Specific Focus of Attention.

Abstract

In many real-world tasks, the ability to focus attention on the important features of the input is crucial for good performance. In this paper a mechanism for achieving task-specific focus of attention is presented. A saliency map, which is based upon a computed expectation of the contents of the inputs at the next time step, indicates which regions of the input retina are important for performing the task. The saliency map can be used to accentuate the features which are important, and de-emphasize those which are not. The performance of this method is demonstrated on a real-world robotics task: autonomous road following. The applicability of this method is also demonstrated in a non-visual domain. Architectural and algorithmic details are provided, as well as empirical results. (KAR) P. 1

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Document Details

Document Type
Technical Report
Publication Date
May 22, 1995
Accession Number
ADA296386

Entities

People

  • Dean Pomerleau
  • Shumeet Baluga

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Autonomous Navigation
  • Computer Science
  • Computer Vision
  • Control Systems
  • Detection
  • Detectors
  • Guidance
  • Image Processing
  • Information Processing
  • Information Systems
  • Neural Networks
  • Psychology
  • Warning Systems

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Vision Science/Vision Psychology/Cognitive Neuroscience.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Autonomy