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[D] How to train a model that'll generate an "average" image based on a large set of images? (alien.top)

submitted 10 months ago by max_b_jo@alien.top to c/machinelearning@academy.garden

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There's a lot of image generators that'll allow you to generate multiple images based on the input data. Is there something that'll generate an image that's an average of the trained model instead?

Didn't plan on using machine learning for this project but realized it might be an interesting path to explore.

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[–] bob2013sherland@alien.top 1 points 10 months ago

Maybe train a Generational adversarial network (GAN) with your own data, that way you can control what it learns / generates

[–] nikgeo25@alien.top 1 points 10 months ago (1 children)

If the model has the following format:

Encoder, diffusion in latent space, decoder

you can average the embedding right before the decoder. Just make sure your averaging is of a representation that can be meaningfully averaged. E.g. averaging images would result in garbage, but their corresponding embeddings might provide something useful.

[–] max_b_jo@alien.top 1 points 10 months ago

Yup, it's pretty possible that the generated image will result in pure garbage. But it's worth exploring and might be a nice visualization!

[–] PM_ME_YOUR_BAYES@alien.top 1 points 10 months ago

Why not averaging the aligned data then?

[–] muxamilian@alien.top 1 points 10 months ago

My idea was to train a GAN and sample the center point: Just input a vector of all 0s into the generator after training: https://muxamilian.github.io/Robo99/

[–] fulowa@alien.top 1 points 10 months ago

this will just produce garbage no?