Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding

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AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks (2020, neutral): “We evaluate the searched/compressed model on several popular Algorithm 1 The Proposed AutoGAN-Distiller Framework 1: Input: dataset χ = { x i } Ni =1 , pretrained generator G 0 , search space and supernet G , epochs to pretrain ( T 1 ), search ( T 2 ) and train-from-scratch ( T 3 ) 2: Output: traine”
A Deep Journey into Super-resolution (2019, methodology): “In this section, we compare the state-of-the-art algorithms on publicly available benchmark datasets which include Set5 [88], Set14 [89], BSD100 [90], Urban100 [91], DIV2K [50] and Manga109 [92].”
Image Super-Resolution via Dual-State Recurrent Networks (2018, methodology): “To evaluate the proposed DSRN algorithm, we train our model using 91 images proposed in [44] and test on the following datasets: Set5 [4], Set14 [46], B100 [30] and Urban100 [19].”