CNN and self-attention

• CNN], which could only accept fixed-length input, has been replaced by attention mechanism, which can accept indefinite-length input.

• Why can we extend to indefinite length?

• The CNN was hard-coded in the form of a matrix to determine which position values were multiplied by what weight, relative to itself

  • So it was necessary to fix in advance how many values to process before and after .

• The attention mechanism determines what weights to multiply by the value of the

  • So there’s no need to predetermine the number of pieces.

  • 

  • Instead, the value returned by the attention mechanism is the same even if the input columns are shuffled because there is no position information in the simple configuration

• Transformer combines [Positional Encoding

  • Embed location information in the input value itself.
  • Now the attention mechanism can take the place of CNN.

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