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I'm not OP, but my take on this is that it would be quite difficult to design a DNN working directly with vector graphics. I'd say you should rasterize your image before inputting it to the network. Same with outputs. CNNs would definitely struggle with continuous images. This is from what I personally know, and I did not bother to google before writing this, so I might also be very wrong.

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Hi jefft255,

I think you are totally right. As far as I understand, CNNs rely on the correlation of neighbouring pixels. And so directly applying them to vector graphics won't work.

However, vectorisation has massive downsides as well.

I will try to summarise it in some article. This had nerd-sniped my brain and so I need to write it down. Even if I have no solution for the problem yet.

Happy to send you a link to the draft once I have it so you can comment.

In my pretty untrained opinion, it feels like something that outputs vector images might well be trained using reinforcement learning, since it seems like the problem can be well modeled with a policy: "given what I want to produce and what I've produced so far, the next vector object I want to draw is x"

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