<p>In more recent work [1], it was found that you can use LayerNorm in CNNs without degrading accuracy, though it depends on the model architecture.  Liu et al. [1] found while developing ConvNeXt that &quot;Directly substituting LN for BN in the original ResNet will result in suboptimal performance&quot; but they observed that their ConvNeXt &quot;model does not have any difficulties training with LN; in fact, the performance is slightly
better&quot;.</p>
<p>It would be great if there were a better explanation as to why...</p>
<ol>
<li>Liu et al. A ConvNet for the 2020s. <a href="https://arxiv.org/pdf/2201.03545.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/2201.03545.pdf</a></li>
</ol>
Can I use Layer Normalization with CNN?
<p>I see the Layer Normalization is the modern normalization method than Batch Normalization, and it is very simple to coding in Tensorflow.
But I think the layer normalization is designed for RNN, and the batch normalization for CNN.
Can I use the layer normalization with CNN that process image classification task?
What are the criteria for choosing batch normalization or layer?</p>