论文标题
基于热图的离分布检测
Heatmap-based Out-of-Distribution Detection
论文作者
论文摘要
我们的工作将分布外（OOD）检测视为神经网络输出解释问题。我们学习一个用于检测OOD图像的热图表示，同时可视化分布式图像区域。鉴于训练有素的固定分类器，我们根据分类器特征和类预测，训练一个解码器神经网络，以产生零响应的热图和OOD样品的高响应热图。我们的主要创新在于对OOD样本的热图定义，作为与最接近分布样本的归一化差异。热图可以作为区分内部和分布样品的边缘。我们的方法不仅生成用于OOD检测的热图，而且还指示输入图像的分布区域和分布区域。在我们的评估中，我们的方法主要优于先前在CIFAR-10，CIFAR-100和Tiny Imagenet的固定分类器上的工作。该代码可公开可用：https：//github.com/jhornauer/heatmap_ood。
Our work investigates out-of-distribution (OOD) detection as a neural network output explanation problem. We learn a heatmap representation for detecting OOD images while visualizing in- and out-of-distribution image regions at the same time. Given a trained and fixed classifier, we train a decoder neural network to produce heatmaps with zero response for in-distribution samples and high response heatmaps for OOD samples, based on the classifier features and the class prediction. Our main innovation lies in the heatmap definition for an OOD sample, as the normalized difference from the closest in-distribution sample. The heatmap serves as a margin to distinguish between in- and out-of-distribution samples. Our approach generates the heatmaps not only for OOD detection, but also to indicate in- and out-of-distribution regions of the input image. In our evaluations, our approach mostly outperforms the prior work on fixed classifiers, trained on CIFAR-10, CIFAR-100 and Tiny ImageNet. The code is publicly available at: https://github.com/jhornauer/heatmap_ood.
