Batch normalization does not improve initialization
Joris Dannemann; Gero Junike · 2025 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Batch normalization is one of the most important regularization techniques for neural networks, significantly improving training by centering the layers of the neural network. There have been several attempts to provide a theoretical justification for batch ormalization. Santurkar and Tsipras (2018) [How does batch normalization help optimization? Advances in neural information rocessing systems, 31] claim that batch normalization improves initialization. We provide a counterexample showing that this claim s not true, i.e., batch normalization does not improve initialization.
Excerpt shown for reference under fair use — read the full paper at the publisher.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Leakage and the reproducibility crisis in machine-learning-based science
Negative / Null Result ReportDefining and detecting quantum speedup
Negative / Null Result ReportService robots in hotels: understanding the service quality perceptions of human-robot interaction
Negative / Null Result ReportBoosting methods for multi-class imbalanced data classification: an experimental review
Negative / Null Result ReportFINANCIAL DEVELOPMENT AND ECONOMIC GROWTH: A META‐ANALYSIS
Negative / Null Result ReportThe impact of site-specific digital histology signatures on deep learning model accuracy and bias
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: arXiv