Maybe train a Generational adversarial network (GAN) with your own data, that way you can control what it learns / generates
this post was submitted on 31 Oct 2023
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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.
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!
Why not averaging the aligned data then?
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/
this will just produce garbage no?