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Everything about Batch normalization
Method of improving artificial neural network
In artificial neural networks, batch normalization is a normalization technique used to make training faster and more stable by adjusting the inputs to each layer—re-centering them around zero and re-scaling them to a standard size. It was introduced by Sergey Ioffe and Christian Szegedy in 2015. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Batch normalizationA 5-minute orientation
- Skim a recent paper: TFormer: A time–frequency Transformer with batch normalization for driver fatigue recognitionInfluential work since 2022
- Pick up a classic book: Introductory statistics for the behavioral sciencesThe most-reprinted book on the subject
- Read the landmark paper: Adaptive Batch Normalization for practical domain adaptationCited 556 times
- Go sideways: Normalization (machine learning)A closely related topic
- Go sideways: Online machine learningA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Adaptive Batch Normalization for practical domain adaptation
Deep learning based on Batch Normalization for P300 signal detection
$L1$ -Norm Batch Normalization for Efficient Training of Deep Neural Networks
Batch normalization embeddings for deep domain generalization
Revisiting Internal Covariate Shift for Batch Normalization
Instance Enhancement Batch Normalization: An Adaptive Regulator of Batch Noise
Re-Thinking the Effectiveness of Batch Normalization and Beyond
Training Binary Neural Network without Batch Normalization for Image Super-Resolution
🆕 Influential since 2022
TFormer: A time–frequency Transformer with batch normalization for driver fatigue recognition
Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN)
Why Batch Normalization Damage Federated Learning on Non-IID Data?
Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization
Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-identification
📚 Books worth reading
Introductory statistics for the behavioral sciencesJoan Welkowitz, Barry H. Cohen · 1971
How Linux WorksBrian Ward · 2004
Pythonで学ぶディープラーニングの理論と実装 · 2016
Quantum computer scienceMarco Lanzagorta · 2008
Math everywhereMartin Burger · 2007
Batch effects and noise in microarray experiments, sources, and solutionsAndreas Scherer · 2009- 📕Statistical issues in microarray data analysisYu Guo · 2006
Fundamentals of Deep LearningNikhil Buduma · 2017
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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