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Are there any open sourced algorithms that can take a couple of images as an input and generate a new, similar image based on that input? Or are there any resources where I can learn to create such an algorithm?

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I'm not an expert on that so you could probably get a better answer.

I'm not sure to understand what you're looking for. Are the couple of images about the same thing? Like pictures of cats and you want to generate a new cat based on these pictures? If that's what you want, you could probably take a look at Generative Adversarial Network (GAN) : Introduction. A GAN is made up of a Generator and a Discriminator. The goal of the discriminator is to distinguish the real data from the generated data. And the goal of the generator is to improve its generated data to look similar to real data. Then, if there are different cat images in your dataset, the generator will learn to create a new cat based on that dataset.

If what you're looking for is to take different images like a cat and a dog and generate a "catdog", you can take a look at Variational AutoEncoder (VAE). For example you can train two different VAE (Encoder/Decoder). One for cats, and one for dogs. Then you take the encoder of dogs and the decoder of cats. That what I saw one day, not sure if it really works.

Correct me if I'm wrong

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  • $\begingroup$ Thank you, that sounds like a good starting point. Actually it is about similiar images. Specifically, I want to feed the algorithm with pictures of stellar nebula and I am hoping to create new pictures of nebula by the algorithm. It's for a game project that I am working on. $\endgroup$ – Mark May 22 at 9:12
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    $\begingroup$ It probably exists some other methods that I don't know but GAN seems to be good here. To get a better idea, you can read this article ( towardsdatascience.com/…) and replace Homer Simpson by stellar nebula. $\endgroup$ – Dlmss May 22 at 9:27

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