Real-time detection of anomalies in streaming data is receiving increasing
attention as it allows us to raise alerts, predict faults, and detect
intrusions or threats across industries. Yet, little attention has been given
to compare the effectiveness and efficiency of anomaly detectors for streaming
data (i.e., of online algorithms). In this paper, we present a qualitative,
synthetic overview of major online detectors from different algorithmic
families (i.e., distance, density, tree or projection-based) and highlight
their main ideas for constructing, updating and testing detection models. Then,
we provide a thorough analysis of the results of a quantitative experimental
evaluation of online detection algorithms along with their offline
counterparts. The behavior of the detectors is correlated with the
characteristics of different datasets (i.e., meta-features), thereby providing
a meta-level analysis of their performance. Our study addresses several missing
insights from the literature such as (a) how reliable are detectors against a
random classifier and what dataset characteristics make them perform randomly;
(b) to what extent online detectors approximate the performance of offline
counterparts; (c) which sketch strategy and update primitives of detectors are
best to detect anomalies visible only within a feature subspace of a dataset;
(d) what are the tradeoffs between the effectiveness and the efficiency of
detectors belonging to different algorithmic families; (e) which specific
characteristics of datasets yield an online algorithm to outperform all others.

本文提供了对不同算法家族的主要在线探测器的质量、综合概述，包括构建、更新和测试探测模型的主要思想，并提供了在线检测算法与离线对应算法的结果定量实验评估的彻底分析，以及对数据集（即元特征）的各种特征进行统计分析。

在线异常检测器的元层分析

A Meta-level Analysis of Online Anomaly Detectors

Detecting out-of-distribution examples is important for safety-critical
machine learning applications such as detecting novel biological phenomena and
self-driving cars. However, existing research mainly focuses on simple
small-scale settings. To set the stage for more realistic out-of-distribution
detection, we depart from small-scale settings and explore large-scale
multiclass and multi-label settings with high-resolution images and thousands
of classes. To make future work in real-world settings possible, we create new
benchmarks for three large-scale settings. To test ImageNet multiclass anomaly
detectors, we introduce the Species dataset containing over 700,000 images and
over a thousand anomalous species. We leverage ImageNet-21K to evaluate PASCAL
VOC and COCO multilabel anomaly detectors. Third, we introduce a new benchmark
for anomaly segmentation by introducing a segmentation benchmark with road
anomalies. We conduct extensive experiments in these more realistic settings
for out-of-distribution detection and find that a surprisingly simple detector
based on the maximum logit outperforms prior methods in all the large-scale
multi-class, multi-label, and segmentation tasks, establishing a simple new
baseline for future work.

本研究探讨了基于大规模分类器和高分辨率图像的复杂场景下的异常检测，为未来真实世界的应用提供了新的基准，并发现简单的最大对数检测器在所有大规模多类别、多标签和分割任务中均优于之前的方法，成为未来工作的一个新基线。

面向实际场景的未知分布检测扩展

Scaling Out-of-Distribution Detection for Real-World Settings

Anomaly detectors are often used to produce a ranked list of statistical
anomalies, which are examined by human analysts in order to extract the actual
anomalies of interest. Unfortunately, in realworld applications, this process
can be exceedingly difficult for the analyst since a large fraction of
high-ranking anomalies are false positives and not interesting from the
application perspective. In this paper, we aim to make the analyst's job easier
by allowing for analyst feedback during the investigation process. Ideally, the
feedback influences the ranking of the anomaly detector in a way that reduces
the number of false positives that must be examined before discovering the
anomalies of interest. In particular, we introduce a novel technique for
incorporating simple binary feedback into tree-based anomaly detectors. We
focus on the Isolation Forest algorithm as a representative tree-based anomaly
detector, and show that we can significantly improve its performance by
incorporating feedback, when compared with the baseline algorithm that does not
incorporate feedback. Our technique is simple and scales well as the size of
the data increases, which makes it suitable for interactive discovery of
anomalies in large datasets.

本文提出了一种新的方法，利用二进制反馈来改进基于树的异常检测算法（Isolation Forest Algorithm）的性能，从而减少高排名误报的数量，使得人工分析者可以更容易地发现感兴趣的异常点，特别适用于大数据集的交互式异常发现。