We present Gradient Activation Maps (GAM) - a machinery for explaining predictions made by visual similarity and classification models. By gleaning localized gradient and activation information from multiple network layers, GAM offers improved visual explanations, when compared to existing alternatives. The algorithmic advantages of GAM are explained in detail, and validated empirically, where it is shown that GAM outperforms its alternatives across various tasks and datasets.

翻译：我们展示了梯度活化地图(GAM),这是一个解释视觉相似性和分类模型预测的机制。 通过从多个网络层收集本地梯度和激活信息,GAM提供了更好的视觉解释,与现有的替代方法相比。 GAM的算法优势得到了详细解释和实证,其中显示GAM在各种任务和数据集中优于其替代方法。