What's the use of dilated convolutions?
<p>I refer to <a href="https://arxiv.org/abs/1511.07122" rel="noreferrer">Multi-Scale Context Aggregation by Dilated Convolutions</a>.</p>

<ul>
<li>A 2x2 kernel would have holes in it such that it becomes a 3x3 kernel.</li>
<li>A 3x3 kernel would have holes in it such that it becomes a 5x5 kernel.</li>
<li>Above assumes interval 1 of course.</li>
</ul>

<p>I can clearly see that this allows you to effectively use 4 parameters but have a receptive field of 3x3 and 9 parameters but have a receptive field of 5x5. </p>

<p>Is the case of dilated convolution simply to save on parameters while reaping the benefit of a larger receptive field and thus save memory and computations?</p>