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Improved differentiable neural architecture search for single image super-resolution
2021
Peer-to-Peer Networking and Applications
AbstractDeep learning has shown prominent superiority over other machine learning algorithms in Single Image Super-Resolution (SISR). In order to reduce the efforts and resources cost on manually designing deep architecture, we use differentiable neural architecture search (DARTS) on SISR. Since neural architecture search was originally used for classification tasks, our experiments show that direct usage of DARTS on super-resolutions tasks will give rise to many skip connections in the search
doi:10.1007/s12083-020-01048-4
fatcat:eels5wqcxvattjdkixiynvhqni