2024/10/08 by Abdolazimi, Reyhaneh, Jin, Shengmin, Varshney, Pramod K. +1
#FOS: Computer and information sciences #Machine Learning (cs.LG)
paper · doi:10.48550/arxiv.2410.06348
Noise, traditionally considered a nuisance in computational systems, is reconsidered for its unexpected and counter-intuitive benefits across a wide spectrum of domains, including nonlinear information processing, signal processing, image processing, machine learning, network science, and natural language processing. Through a comprehensive review of both historical and contemporary research, this survey presents a dual perspective on noise, acknowledging its potential to both disrupt and enhance performance. Particularly, we highlight how noise-enhanced training strategies can lead to models that better generalize from noisy data, positioning noise not just as a challenge to overcome but as a strategic tool for improvement. This work calls for a shift in how we perceive noise, proposing that it can be a spark for innovation and advancement in the information era.