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new_name_who_dis_ t1_ispgfn7 wrote

I'm currently working on a similar problem and my current approach is to add some noise (but not a lot) on the image in domain A, and then denoise with network trained on generating images in domain B.

It's not perfect, but it works. I'd be interested to hear more discussion of this topic.

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cygn t1_isx84ko wrote

could you show some images please?

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new_name_who_dis_ t1_isy3qr2 wrote

It's proprietary data so I can't. If you have a public dataset (or I guess 2 for style/domain transfer), I could run my code on it and get back to you.

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cygn t1_it25agy wrote

you could use one of the datasets that are listed here: https://paperswithcode.com/task/domain-adaptation

Office-31 for example looks quite practical. It has product images from Amazon, DSLR and webcam images. The problems I'd like to solve are similar. Take good images of plants with diseases and adapt them to images taken from users with smartphones with lots of quality issues.

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Tomsen1410 t1_isjkhp1 wrote

Unlike GANs however, diffusion models are trained in a supervised fashion (they need an image which gets denoised). Not sure how one would use them for unsupervised translation like e.g. CycleGAN.

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SeucheAchat9115 t1_iskb0z2 wrote

Maybe with artificial noise? But I am not familiar with how diffusion is really trained, maybe thats how diffusion works.

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SeucheAchat9115 t1_isjcivg wrote

Would be interesting for a comparison. Since Diffusion Based Models seems like performing bad for faces, it might be interesting to see it perform image to image for synth to real like cycle-gan

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AljoSt t1_isjbi3f wrote

Have not seen any projects, but tried it myself. Definitely looks promising, but didn't have much time to investigate fully

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