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arXiv · 2012.06311

Differentiable Histogram with Hard-Binning

Ibrahim Yusuf·George Igwegbe·Oluwafemi Azeez
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Abstract

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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Ibrahim Yusuf, George Igwegbe, Oluwafemi Azeez. 2020-11-20. Differentiable Histogram with Hard-Binning. https://arxiv.org/abs/2012.06311

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