e49b8b4053df9505e1f48c3a701c0682-Reviews.html

We thank all reviewers for helpful comments. The key novelty of our method is to address this limitation by (1) computing optimal column weights via solving a quadratic program and (2) training a separate network to predict the optimal weights. Our method is generally applicable to not only denoising, but also many other problems. In our experiments, this single AMC-SSDA outperformed (on average) other baseline SSDAs trained from any specific type of noise (e.g., Gaussian, salt & pepper, or speckle) or the mixture of all these noise types. In additional control experiment, for each "seen" noise type that was tested on in the paper, we trained an SSDA with that exact noise type, including the exact statistics of the noise; let's call this the "informed-SSDA".