论文标题
在深空中很远：密集的离分布检测
Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection
论文作者
论文摘要
分发检测的关键是分布数据或其特征表示的密度估计。对于分布数据具有复杂的基础结构的域中，这对于密集的异常检测尤其具有挑战性。最近显示，最近的邻居方法在以对象数据域（例如工业检查和图像分类）中很好地工作。在本文中，我们表明，在使用适当的特征表示时，最近邻居的方法还会在复杂的驾驶场景中产生最新的新颖性检测结果。特别是，我们发现基于变压器的体系结构产生的表示形式可以为任务带来更好的相似性指标。我们将这些模型的多头结构确定为原因之一，并展示了将一些改进转移到CNN的方法。最终，该方法是简单且无创的，即，它不会影响主要的分割性能，避免了异常示例的训练，并取得了对路态，strethazazards和SementMeifyoucan-Anomaly的最先进结果。
The key to out-of-distribution detection is density estimation of the in-distribution data or of its feature representations. This is particularly challenging for dense anomaly detection in domains where the in-distribution data has a complex underlying structure. Nearest-Neighbors approaches have been shown to work well in object-centric data domains, such as industrial inspection and image classification. In this paper, we show that nearest-neighbor approaches also yield state-of-the-art results on dense novelty detection in complex driving scenes when working with an appropriate feature representation. In particular, we find that transformer-based architectures produce representations that yield much better similarity metrics for the task. We identify the multi-head structure of these models as one of the reasons, and demonstrate a way to transfer some of the improvements to CNNs. Ultimately, the approach is simple and non-invasive, i.e., it does not affect the primary segmentation performance, refrains from training on examples of anomalies, and achieves state-of-the-art results on RoadAnomaly, StreetHazards, and SegmentMeIfYouCan-Anomaly.
