I wanted to point you to a recent paper on the arXiv:
I think you'll like Figure 1 in particular.Apparently, GANs provide signal models that allow for extremely good denoising in a high-noise regime. To denoise, we hunt for the point in the GAN model that's closest to the noisy image. Surprisingly, local minimization works well in practice. To help explain this, we provide theory for a certain model of neural networks using techniques from spherical harmonics. This is joint work with Soledad Villar (NYU).Cheers,Dustin
Yes, you're right, I do like Figure 1 !
It has been experimentally established that deep neural networks can be used to produce good generative models for real world data. It has also been established that such generative models can be exploited to solve classical inverse problems like compressed sensing and super resolution. In this work we focus on the classical signal processing problem of image denoising. We propose a theoretical setting that uses spherical harmonics to identify what mathematical properties of the activation functions will allow signal denoising with local methods.
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