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I was experimenting with some CNN models and reading research material when I realized that it could happen that using only a single batch normalization layer at the early stages of the network could be beneficial compared to using a batch normalization layer after each convolutional layer (in case of CNNs).

The inspiration came from the paper Comparison of feature learning methods for human activity recognition using wearable sensors by F. Li, K. Shirahama, M. A. Nisar, L. Koping, and M. Grzegorzek.

I was wondering when and why does batch normalization hurt learning? Why using a single batch normalization instead of many may result in better learning?

Thanks

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