论文标题
Grad-CAM ++等效于具有正梯度的Grad-CAM
Grad-CAM++ is Equivalent to Grad-CAM With Positive Gradients
论文作者
论文摘要
Grad-CAM算法提供了一种方法来确定图像的哪些部分对分类器深网的输出最大的贡献。该算法是简单的，并且广泛用于图像中对象的定位，尽管一些研究人员指出了其局限性，并提出了各种替代方案。其中之一是Grad-CAM ++，根据其作者可以为网络预测提供更好的视觉解释，即使是单个图像中多个对象实例的出现，也可以更好地定位对象。在这里，我们表明，Grad-CAM ++实际上等同于Grad-CAM的非常简单的变化，在该变化中，梯度被阳性梯度代替。
The Grad-CAM algorithm provides a way to identify what parts of an image contribute most to the output of a classifier deep network. The algorithm is simple and widely used for localization of objects in an image, although some researchers have point out its limitations, and proposed various alternatives. One of them is Grad-CAM++, that according to its authors can provide better visual explanations for network predictions, and does a better job at locating objects even for occurrences of multiple object instances in a single image. Here we show that Grad-CAM++ is practically equivalent to a very simple variation of Grad-CAM in which gradients are replaced with positive gradients.
