<p>In addition to the benefits you already mentioned such as larger <strong>receptive field</strong>, <strong>efficient computation</strong> and <strong>lesser memory consumption</strong>, the dilated causal convolutions also has the following benefits:</p>

<ul>
<li>it <strong>preserves the resolution/dimensions of data</strong> at the output layer. This is because the layers are dilated instead of pooling, hence the name <em>dilated causal convolutions</em>.</li>
<li>it <strong>maintains the ordering of data</strong>. For example, in 1D dilated causal convolutions when the prediction of output depends on previous inputs then the structure of convolution helps in maintaining the ordering of data. </li>
</ul>

<p>I'd refer you to read this amazing paper <a href="https://arxiv.org/pdf/1609.03499.pdf" rel="noreferrer">WaveNet</a> which applies dilated causal convolutions to raw audio waveform for generating speech, music and even recognize speech from raw audio waveform.</p>

<p>I hope you find this answer helpful.</p>
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>