<p>Referring to <a href="https://arxiv.org/abs/1511.07122" rel="nofollow noreferrer">Multi-Scale Context Aggregation by Dilated Convolutions</a>, yes, you can save some memory while having a larger receptive field. You might want to use dilated convolutions if you want an exponential expansion of the receptive field without loss of resolution or coverage. This allows us to have a larger receptive field with the same computation and memory costs while preserving resolution. Pooling and stride Convolutions can also kind of &quot;expand&quot; the receptive field, but those reduce data's resolution.</p>
<p>Generally, dilated convolutions have also shown to perform better, for example in image segmentation in <a href="https://arxiv.org/pdf/1606.00915" rel="nofollow noreferrer">DeepLab</a> and in speech in <a href="https://arxiv.org/pdf/1609.03499.pdf" rel="nofollow noreferrer">WaveNet</a>.</p>
<p><a href="https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md" rel="nofollow noreferrer">Here</a> shows a neat visualization of what dilation does.</p>
When to use dilated convolutions?
<p>I do not understand what is the use of dilated convolution and when should we use it. When we want a larger receptive field while saving memory? By increasing <code>dilation</code> size, it increases the spacing between the kernel points?</p>