理解keras的CNN中padding='causal'

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This is a great concise explanation about what is “causal” padding:

One thing that Conv1D does allow us to specify is padding=“causal”. This simply pads the layer’s input with zeros in the front so that we can also predict the values of early time steps in the frame:

Dilation just means skipping nodes. Unlike strides which tells you where to apply the kernel next, dilation tells you how to spread your kernel. In a sense, it is equivalent to a stride in the previous layer.

In the image above, if the lower layer had a stride of 2, we would skip (2,3,4,5) and this would have given us the same results.

reference:
Causal padding in keras
Convolutions in Autoregressive Neural Networks

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