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I'm playing around with TCN's lately and I don't understand one thing. How is the receptive field different from the input size?

I think that the receptive field is the time window that TCN considers during the prediction, so I guess the input size shall be equal to it.

According to the WaveNet paper, I cannot see a reason why it should be otherwise. I'm using TensorFlow with this custom library.

Please help me understand.

nbro
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MASTER OF CODE
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1 Answers1

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As you can see in Fig. 2 of the WaveNet paper the receptive field is 5, but the input size is larger (16). The receptive field defines what a single output neuron can see (see arrows in Fig. 2).

enter image description here

The receptive field could also be greater than the input, e.g. if you want to use or you only have the last 12 time steps and use the following structure (WaveNet paper, Fig 3), which can cover different powers of two depending on the number of layers.

enter image description here

If you want to calculate not only the last output neuron, it can make sense that the input size is larger than the receptive field, as the outputs before use also older inputs (see dashed lines).

dexteritas
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