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Kuchenkiller t1_jc4t81a wrote

If you have enough images of defects but are just lacking the labeling (probably easier to come by) one approach is to generate random morphological structures on your bottles (e.g. just some random circles and ellipses) and then apply cycleGAN or CUT to transform from this "segmented" image domain to the domain of real images. As said, you still need a lot of data but don't need labelling. Just generating useful data from noise (basic GAN idea) can work in theory but is extremely hard to train. I had way more success with the domain transfer approach (my case in medical imaging)

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Kuchenkiller t1_jc4tir0 wrote

Also, what is the reason for deep learning if you don't have the data? Have classical methods been unsuccessful?

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Tekno-12345 OP t1_jc5wf2t wrote

Thanks for the reply.
Yes classical methods did not generalize well on foreign data.
I don't have experience on GANs and wanted some experienced opinions.

I have some leads now, I'll try them out and get back to your comment if they did not work out.

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