<p>It should still work with somewhat similar results. There are no problems with skipping only one layer.</p>

<p>See <a href="https://openreview.net/forum?id=HkwBEMWCZ" rel="nofollow noreferrer">https://openreview.net/forum?id=HkwBEMWCZ</a>
and densenets <a href="https://arxiv.org/abs/1608.06993" rel="nofollow noreferrer">https://arxiv.org/abs/1608.06993</a></p>
Why each block in Deep residual network has two convolutional layers instead of one?
<p><a href="https://i.stack.imgur.com/TfMe9.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/TfMe9.png" alt="enter image description here"></a></p>

<p>This figure shows a basic block of a residual network. What it has two convolutional layers? What will happen when it has only one convolutional layer?</p>