What's the use of dilated convolutions?

Question

I refer to Multi-Scale Context Aggregation by Dilated Convolutions.

• A 2x2 kernel would have holes in it such that it becomes a 3x3 kernel.
• A 3x3 kernel wo

Answer 1

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

• it preserves the resolution/dimensions of data at the output layer. This is because the layers are dilated instead of pooling, hence the name dilated causal convolutions.
• it maintains the ordering of data. 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.

I'd refer you to read this amazing paper WaveNet which applies dilated causal convolutions to raw audio waveform for generating speech, music and even recognize speech from raw audio waveform.

I hope you find this answer helpful.