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Does the number of outputs of a neural network have to be smaller than the number of inputs? This seems to be the case in most of the applications that I have seen . At the same time, I am not sure how this might be the case in language translation models. It is not guaranteed that the output (the translation of a text) would be the same or smaller length as the input text. It can be longer or shorter.

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  • $\begingroup$ You can imagine a toy example of this where you are classifying 32x32 pixel images (1024 pixel inputs) into, say, 2000 different categories. That's a somewhat awkward example, but I'm sure there are expansions of that idea that actually provide value in certain domains. $\endgroup$
    – user
    Aug 26 '20 at 4:44
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No! There is no limit whatsoever on the size of the output relative to the size of the input.

But in most cases, a higher number of outputs is not necessary at all.

In the case of language translation models: this is either done fragment by fragment, or the output has a fixed maximum size (e.g. 2x input size).

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