论文标题
用硬式衬里的可微分直方图
Differentiable Histogram with Hard-Binning
论文作者
论文摘要
直方图的简单性和表现性使其在包括深度学习在内的不同环境中成为有用的功能。尽管计算直方图的过程是不可差异的，但研究人员提出了可区分的近似值，这些近似值有一些局限性。提出了一个直接近似传统直方图中硬键操作的可区分直方图。它结合了现有的可区分直方图的强度，并克服了他们的个人挑战。与使用Numpy计算的直方图相比，提出的直方图的绝对近似误差为0.000158。
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.
