噪声不再只是干扰，反而能提升模型泛化能力。
Harnessing the Power of Noise: A Survey of Techniques and Applications
- 将噪声视为可利用的资源，而非需要消除的干扰。
- 噪声增强训练策略让模型在嘈杂数据中表现更好。
- 适合对鲁棒性、泛化能力感兴趣的研习者。
噪声在计算系统中传统上被视为干扰，但其在非线性信息处理、信号处理、图像处理、机器学习、网络科学及自然语言处理等多个领域展现出意想不到的积极作用。本文综述了历史与当代研究，从破坏与增强双重角度审视噪声，特别强调噪声增强训练策略能提升模型对噪声数据的泛化能力。这表明噪声不仅是需克服的挑战，更可作为推动创新的战略工具。该工作呼吁重新认识噪声，主张其在信息时代具有促进技术进步的潜力。
原文摘要 · Abstract (English)
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.
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