  Tuning parameters of a non-differentiable or black-box compute is
challenging. Existing methods rely mostly on random sampling or grid sampling
from the parameter space. Further, with all the current methods, it is not
possible to supply any input specific parameters to the black-box. To the best
of our knowledge, for the first time, we are able to learn input-specific
parameters for a black box in this work. As a test application we choose a
popular image denoising method BM3D as our black-box compute. Then, we use a
differentiable surrogate model (a neural network) to approximate the black-box
behaviour. Next, another neural network is used in an end-to-end fashion to
learn input instance-specific parameters for the black-box. Drawing inspiration
from the work of Tseng et al. [1] , we applied our method to the Smartphone
Image Denoising Dataset (SIDD) for image denoising. The results are compelling,
demonstrating a significant increase in PSNR and a notable improvement in SSIM
nearing 0.93. Experimental results underscore the effectiveness of our approach
in achieving substantial improvements in both model performance and
optimization efficiency. For code and implementation details, please refer to
our GitHub repository.
  [1] Ethan Tseng, Felix Yu, Yuting Yang, Fahim Mannan, Karl St. Arnaud, Derek
Nowrouzezahrai, Jean-Francois Lalonde, and Felix Heide. Hyperparameter
optimization in black-box image processing using differentiable proxies. ACM
Transactions on Graphics (TOG), 38(4), 7 2019.
