<p>A good overview of the different norms is shown in the <a href="https://arxiv.org/abs/1803.08494" rel="nofollow noreferrer">Group Normalization paper</a>.</p>

<p><a href="https://i.stack.imgur.com/EBEms.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/EBEms.png" alt="Norms overview"></a></p>

<p>Instance normalisation is summarised as:</p>

<blockquote>
  <p>[...] IN computes µ and σ along the (H, W) axes for each sample and each channel.</p>
</blockquote>

<p>The mean and standard deviation are computed on the spatial dimensions (H, W) only and are independent of the batch size and channels (there are <em>N x C</em> different norms). Hence, you can use it with a batch size of 1.</p>
Instance Normalization with batch size 1
<p>I am really confused with the meaning of Instance Norm and whether I can use it with a batch size of 1. I am using PyTorch and nothing in the documentation says that batch size should be greater than 1.</p>

<p>I know that for BatchNorm the performance is adversely affected when batch size is less than 8 and hence it puts a sort of soft bound on the batch size. However, I did not see any such analysis on Instance Norm and am a bit confused now. Should I remove the norm layer if my batch size is 1 then?</p>