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Harnessing the Power of Noise: A Survey of Techniques and Applications

arXiv 2410.06348 ↗First posted 8 Oct 2024cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 161 references that are in the map. First posted on arXiv 8 Oct 2024.

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27 of 161 references.

References · 27

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  3. 2017SmoothGrad: removing noise by adding noisecited by 1,109
  4. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,060
  5. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 807
  6. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 680
  7. 2013Speech Recognition with Deep Recurrent Neural Networkscited by 669
  8. 2020Up or Down? Adaptive Rounding for Post-Training Quantizationcited by 621
  9. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 545
  10. 2024The Power of Noise: Redefining Retrieval for RAG Systemscited by 281
  11. 2020Data Augmentation for Graph Neural Networkscited by 203
  12. 2020Random Features Strengthen Graph Neural Networkscited by 186
15 more
  1. 2015Adding Gradient Noise Improves Learning for Very Deep Networkscited by 145
  2. 2023NEFTune: Noisy Embeddings Improve Instruction Finetuningcited by 100
  3. 2020Feedback Loop and Bias Amplification in Recommender Systemscited by 90
  4. 2021Simple GNN Regularisation for 3D Molecular Property Prediction & Beyondcited by 44
  5. 2018Text Data Augmentation Made Simple By Leveraging NLP Cloud APIscited by 40
  6. 2019Improving Robustness of Machine Translation with Synthetic Noisecited by 36
  7. 2014Analyzing noise in autoencoders and deep networkscited by 25
  8. 2021Noise Conditional Flow Model for Learning the Super-Resolution Spacecited by 19
  9. 2019A Good Sample is Hard to Find: Noise Injection Sampling and Self-Training for Neural Language Generation Modelscited by 17
  10. 2020Heatmap Regression via Randomized Roundingcited by 15
  11. 2023Data Augmentation in Training CNNs: Injecting Noise to Imagescited by 10
  12. 2021Topological Regularization for Graph Neural Networks Augmentationcited by 8
  13. 2024GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMscited by 8
  14. 2023Noisy Exemplars Make Large Language Models More Robust: A Domain-Agnostic Behavioral Analysiscited by 6
  15. 2022Towards Efficient Data-Centric Robust Machine Learning with Noise-based Augmentationcited by 4

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