Learning Instance-Specific Parameters of Black-Box Models using Differentiable Surrogates

Arnisha Khondaker, Nilanjan Ray; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 7429-7438

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. Motivated by prior advances in surrogate-based optimization we applied our method to the Smartphone Image Denoising Dataset (SIDD) and the Color Berkeley Segmentation Dataset (CBSD68) 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: https://github.com/arnisha-k/instance-specific-param.