Bin-wise temperature scaling, applied to per-confidence bins and supplemented by augmented validation samples, reduces expected calibration error relative to temperature scaling across tested image classifiers.
Seven ways to improve example-based single image super resolution
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abstract
In this paper we present seven techniques that everybody should know to improve example-based single image super resolution (SR): 1) augmentation of data, 2) use of large dictionaries with efficient search structures, 3) cascading, 4) image self-similarities, 5) back projection refinement, 6) enhanced prediction by consistency check, and 7) context reasoning. We validate our seven techniques on standard SR benchmarks (i.e. Set5, Set14, B100) and methods (i.e. A+, SRCNN, ANR, Zeyde, Yang) and achieve substantial improvements.The techniques are widely applicable and require no changes or only minor adjustments of the SR methods. Moreover, our Improved A+ (IA) method sets new state-of-the-art results outperforming A+ by up to 0.9dB on average PSNR whilst maintaining a low time complexity.
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Bin-wise Temperature Scaling (BTS): Improvement in Confidence Calibration Performance through Simple Scaling Techniques
Bin-wise temperature scaling, applied to per-confidence bins and supplemented by augmented validation samples, reduces expected calibration error relative to temperature scaling across tested image classifiers.