The complexity and scale of IT systems are increasing dramatically, posing
many challenges to real-world anomaly detection. Deep learning anomaly
detection has emerged, aiming at feature learning and anomaly scoring, which
has gained tremendous success. However, little work has been done on the
thresholding problem despite it being a critical factor for the effectiveness
of anomaly detection. In this paper, we model thresholding in anomaly detection
as a Markov Decision Process and propose an agent-based dynamic thresholding
(ADT) framework based on a deep Q-network. The proposed method can be
integrated into many systems that require dynamic thresholding. An auto-encoder
is utilized in this study to obtain feature representations and produce anomaly
scores for complex input data. ADT can adjust thresholds adaptively by
utilizing the anomaly scores from the auto-encoder and significantly improve
anomaly detection performance. The properties of ADT are studied through
experiments on three real-world datasets and compared with benchmarks, hence
demonstrating its thresholding capability, data-efficient learning, stability,
and robustness. Our study validates the effectiveness of reinforcement learning
in optimal thresholding control in anomaly detection.

本研究通过将阈值问题建模为马尔可夫决策过程，并基于深度 Q 网络提出了一种基于代理的动态阈值 (ADT) 框架，该框架可以用于需要动态阈值的多个系统。通过利用自编码器从复杂输入数据中获取特征表示并生成异常得分，ADT 可以自适应地调整阈值，并显著改善异常检测性能。通过在三个真实数据集上进行实验证明了 ADT 的阈值能力、数据高效学习、稳定性和鲁棒性，并验证了强化学习在异常检测中的最优阈值控制的有效性。

ADT: 基于代理的动态阈值方法进行异常检测

ADT: Agent-based Dynamic Thresholding for Anomaly Detection

The objective of Continual Test-time Domain Adaptation (CTDA) is to gradually
adapt a pre-trained model to a sequence of target domains without accessing the
source data. This paper proposes a Dynamic Sample Selection (DSS) method for
CTDA. DSS consists of dynamic thresholding, positive learning, and negative
learning processes. Traditionally, models learn from unlabeled unknown
environment data and equally rely on all samples' pseudo-labels to update their
parameters through self-training. However, noisy predictions exist in these
pseudo-labels, so all samples are not equally trustworthy. Therefore, in our
method, a dynamic thresholding module is first designed to select suspected
low-quality from high-quality samples. The selected low-quality samples are
more likely to be wrongly predicted. Therefore, we apply joint positive and
negative learning on both high- and low-quality samples to reduce the risk of
using wrong information. We conduct extensive experiments that demonstrate the
effectiveness of our proposed method for CTDA in the image domain,
outperforming the state-of-the-art results. Furthermore, our approach is also
evaluated in the 3D point cloud domain, showcasing its versatility and
potential for broader applicability.

在该文中，作者提出了一种用于 Continual Test-time Domain Adaptation 的 Dynamic Sample Selection（DSS）方法，通过动态阈值、正向学习和反向学习等过程，选择低质量样本并进行联合学习，从而提高模型在图像领域的效果，并展示了其在 3D 点云领域的适用性和潜力。

连续化测试时间域自适应通过动态样本选择

Continual Test-time Domain Adaptation via Dynamic Sample Selection

Recent state-of-the-art method FlexMatch firstly demonstrated that correctly
estimating learning status is crucial for semi-supervised learning (SSL).
However, the estimation method proposed by FlexMatch does not take into account
imbalanced data, which is the common case for 3D semi-supervised learning. To
address this problem, we practically demonstrate that unlabeled data
class-level confidence can represent the learning status in the 3D imbalanced
dataset. Based on this finding, we present a novel class-level confidence based
3D SSL method. Firstly, a dynamic thresholding strategy is proposed to utilize
more unlabeled data, especially for low learning status classes. Then, a
re-sampling strategy is designed to avoid biasing toward high learning status
classes, which dynamically changes the sampling probability of each class. To
show the effectiveness of our method in 3D SSL tasks, we conduct extensive
experiments on 3D SSL classification and detection tasks. Our method
significantly outperforms state-of-the-art counterparts for both 3D SSL
classification and detection tasks in all datasets.

本研究提出了基于类别置信度的三维半监督学习方法，在动态阈值策略和重新采样策略的帮助下，成功地解决了三维不平衡数据状态下监督学习的问题，并在分类和检测任务中得到了显著提高。