When first deploying an anomaly detection system, e.g., to detect
out-of-scope queries in chatbots, there are no observed data, making
data-driven approaches ineffective. Zero-shot anomaly detection methods offer a
solution to such "cold-start" cases, but unfortunately they are often not
accurate enough. This paper studies the realistic but underexplored cold-start
setting where an anomaly detection model is initialized using zero-shot
guidance, but subsequently receives a small number of contaminated observations
(namely, that may include anomalies). The goal is to make efficient use of both
the zero-shot guidance and the observations. We propose ColdFusion, a method
that effectively adapts the zero-shot anomaly detector to contaminated
observations. To support future development of this new setting, we propose an
evaluation suite consisting of evaluation protocols and metrics.

本文研究冷启动情况下的异常检测问题，通过将零样本学习与含有异常的少量观测相结合，提出了一种能够适应这一情况的新方法 ColdFusion，并提供了相应的评估套件。

从零到英雄：冷启动异常检测

From Zero to Hero: Cold-Start Anomaly Detection

As supervised fine-tuning of pre-trained models within NLP applications
increases in popularity, larger corpora of annotated data are required,
especially with increasing parameter counts in large language models. Active
learning, which attempts to mine and annotate unlabeled instances to improve
model performance maximally fast, is a common choice for reducing the
annotation cost; however, most methods typically ignore class imbalance and
either assume access to initial annotated data or require multiple rounds of
active learning selection before improving rare classes. We present STENCIL,
which utilizes a set of text exemplars and the recently proposed submodular
mutual information to select a set of weakly labeled rare-class instances that
are then strongly labeled by an annotator. We show that STENCIL improves
overall accuracy by $10\%-24\%$ and rare-class F-1 score by $17\%-40\%$ on
multiple text classification datasets over common active learning methods
within the class-imbalanced cold-start setting.

利用 STENCIL 方法，在类不平衡冷启动环境下，通过选择一组弱标记的稀有类别实例并由注释者进行强标记来改善文本分类数据集的整体准确性和稀有类别的 F-1 分数，相对于常见的主动学习方法，STENCIL 方法提高了 10% 至 24% 的整体准确性和 17% 至 40% 的稀有类别 F-1 分数。

基于子模块互信息的弱监督适用于冷启动主动学习

STENCIL: Submodular Mutual Information Based Weak Supervision for  Cold-Start Active Learning

Label scarcity has been a long-standing issue for biomedical image
segmentation, due to high annotation costs and professional requirements.
Recently, active learning (AL) strategies strive to reduce annotation costs by
querying a small portion of data for annotation, receiving much traction in the
field of medical imaging. However, most of the existing AL methods have to
initialize models with some randomly selected samples followed by active
selection based on various criteria, such as uncertainty and diversity. Such
random-start initialization methods inevitably introduce under-value redundant
samples and unnecessary annotation costs. For the purpose of addressing the
issue, we propose a novel self-supervised assisted active learning framework in
the cold-start setting, in which the segmentation model is first warmed up with
self-supervised learning (SSL), and then SSL features are used for sample
selection via latent feature clustering without accessing labels. We assess our
proposed methodology on skin lesions segmentation task. Extensive experiments
demonstrate that our approach is capable of achieving promising performance
with substantial improvements over existing baselines.

本文提出了一种新的自我监督辅助主动学习框架，用于解决生物医学图像分割中标签不足的问题，该方法使用自我监督特征聚类来选择样本，实验证明其可在皮肤病变分割任务上取得显著改进。