Review for NeurIPS paper: Unfolding the Alternating Optimization for Blind Super Resolution

Weaknesses: All weaknesses are related to experiments, analysis and understanding. 1. Missing Methods to compare to: - NTIRE'20 leaders in real-SR tracks seems to be a must. Deep Unfolding Network for Image Super-Resolution CVPR'20 (cited [33] but not compared against) - Cornillere et al. Blind Image Super-Resolution with Spatially Variant Degradations SIGA"19 2. Comparisons settings: Setting2- DIV2KRK is a great choice, but only few methods are tested on it. Also- comparison on non-blind setting with bicubic kernel is important to understand if the improvement is in the upscaling or in the kernel estimation. Using GT kernel and compare, try different intializations, ablate architectural elements (what happens if you do the high-level idea using the basic networks introduced in IKC?- this will let us know if the advantage comes from the elegant idea or from an optimized architecture).