In the recent WaveNet paper, the authors refer to their model as having stacked layers of dilated convolutions. They also produce the following charts, explaining the difference between 'regular' convolutions and dilated convolutions.
They then show an architecture used by their model , which they refer to as dilated convolutions. It looks like this. They say that each layer has increasing dilations of (1, 2, 4, 8). But to me this looks like a regular convolution with a filter size of 2 and a stride of 2, repeated for 4 layers.
In the WaveNet diagram, none of the filters skip over an available input. There are no holes. In my diagram ,each filter skips over (d - 1) available inputs. This is how dilation is supposed to work no?
So my question is, which (if any) of the following propositions are correct?
- I don't understand dilated and/or regular convolutions.
- Deepmind did not actually implement a dilated convolution, but rather a strided convolution, but misused the word dilation.
- Deepmind did implement a dilated convolution, but did not implement the chart correctly.
I am not fluent enough in TensorFlow code to understand what their code is doing exactly, but I did post a related question on Stack Exchange, which contains the bit of code that could answer this question.