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Differentiable Histogram with Hard-Binning
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The simplicity and expressiveness of a histogram render it a useful feature in different contexts including deep learning. Although the process of computing a histogram is non-differentiable, researchers have proposed differentiable approximations, which have some limitations. A differentiable histogram that directly approximates the hard-binning operation in conventional histograms is proposed. It combines the strength of existing differentiable histograms and overcomes their individual challenges. In comparison to a histogram computed using Numpy, the proposed histogram has an absolute approximation error of 0.000158.
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Cited by 1 Pith paper
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Rethinking Image Histogram Matching for Image Classification
A differentiable histogram-matching preprocessing whose target distribution is learned end-to-end from normal-weather images improves classification accuracy on unseen fog, rain, sand, and snow images.
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