Noise Enhancement: Techniques and Applications


Noise is often seen as unwanted signal while it has been shown beneficial in many information processing systems and algorithms. Noise enhancement has been utilized in many biological and physical systems, machine learning methods, and deep learning techniques in order to improve efficiency and performance. This tutorial presents (1) the different types of noise; (2) noise applications; (3) noise-enhanced processing systems; (4) noise enhanced learning methods; and (5) noise injection methods in network science.

Tutorial Outline

  • Introduction and Overview (10 minutes)
  • Noise-Enhanced Information Systems (30 minutes)
  • Stochastic Resonance
  • Image Processing
  • Signal Processing
  • Optimization
  • Noise-Enhanced Unsupervised Learning (20 minutes)
  • Clustering and Competitive Learning Algorithms
  • Generative Adversarial Networks
  • Noise as learning target
  • Noise-Enhanced Supervised Learning (20 minutes)
  • Noise as a Regularizer
  • Noise added to inputs, models, and the learning process
  • Effect of noise on training
  • Noise-Enhanced Network Science (30 minutes)
  • Graph perturbation techniques
  • Community Detection
  • Link Prediction


Reyhaneh Abdolazimi

Reyhaneh Abdolazimi is a research assistant of Computer and Information Science and Engineering at Syracuse University. Her research interests include large-scale graph mining and Social Network Analysis. Her work has been published in data mining and machine learning venues. Reyhaneh's work on Noise- Enhancement has been nominated for best paper awards. Before joining Syracuse University, she received the B.S and M.S. degrees in Computer Engineering from Iran University of Science and Technology. More information can be found at [here].

Reza Zafarani

Reza Zafarani received his PhD in computer science from Arizona State University in 2015. He is currently an Assistant Professor at the department of Electrical Engineering and Computer Science at Syracuse University. His research interests are in Data Mining, Machine Learning, Social Media Mining, and Social Network Analysis. His research has been published at major academic venues, and highlighted in various scientific outlets. He is the principal author of "Social Media Mining: An Introduction", a textbook by Cambridge University Press and the associate editor for SIGKDD Explorations and Frontiers in communication. He is the recipient of the NSF CAREER award, the winner of the President’s Award for Innovation and outstanding teaching award at Arizona State University. More information can be found found at [here].
Copyright © Reyhaneh Abdolazimi, 2022