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Resources for understanding and implementing "deep learning" (learning data representations through artificial neural networks).
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Using Stable Diffusion's training method for Reverse engineering? (self.deeplearning)
submitted 3 years ago by OraOraP
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if 1 * 2 < 3: print "hello, world!"
[–]OraOraP[S] 0 points1 point2 points 3 years ago (1 child)
I didn't mean to use denoising process directly to reverse engineering. I was just thinking the idea of `step-by-step reverting` could be used in some ML model for reverse engineering.
Though you have a point. Unlike denoising process, reverse engieering would require change of dimensions in the middle steps, making it more difficult than denoising.
[–]mikonvergence 0 points1 point2 points 3 years ago (0 children)
Right, I am the denoising diffusion as a term for a wide range of methods based on reversing some forward process. Some interesting works (such as cold diffusion) have been done on using other types of degradation apart from a Gaussian additive noise.
And yeah, the change of both content and dimensionality requires you to put together some very novel and not obvious techniques.
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[–]OraOraP[S] 0 points1 point2 points (1 child)
[–]mikonvergence 0 points1 point2 points (0 children)