Review for NeurIPS paper: LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond

Weaknesses: The dictionary used in reconstructing HR images is hand-crafted. Why can the filters in the dictionary not be learned as kernels in neural network and enjoy the benefit of end-to-end learning as many pure deep learning-based SISR method? In the experiment, when comparing with SOTA SISA methods, only x2 and x4 results are shown while x3 results are missing. The authors are recommended to provide x3 results as well. In addition, FALSR-C and FALSR-A in Table 2 used only DIV2K as the training set, while the training set of the proposed method are both DIV2K and Flickr2K, and thus the comparison here is not fair.