Toward generalist anomaly detection via in-context residual learning with few-shot sample prompts
This paper explores the problem of Generalist Anomaly Detection (GAD) aiming to train one
single detection model that can generalize to detect anomalies in diverse datasets from …
single detection model that can generalize to detect anomalies in diverse datasets from …
Anomaly heterogeneity learning for open-set supervised anomaly detection
Open-set supervised anomaly detection (OSAD)-a recently emerging anomaly detection
area-aims at utilizing a few samples of anomaly classes seen during training to detect …
area-aims at utilizing a few samples of anomaly classes seen during training to detect …
Generalad: Anomaly detection across domains by attending to distorted features
In the domain of anomaly detection, methods often excel in either high-level semantic or low-
level industrial benchmarks, rarely achieving cross-domain proficiency. Semantic anomalies …
level industrial benchmarks, rarely achieving cross-domain proficiency. Semantic anomalies …
Domain-independent detection of known anomalies
J Bühler, J Fehrenbach, L Steinmann, C Nauck… - arxiv preprint arxiv …, 2024 - arxiv.org
One persistent obstacle in industrial quality inspection is the detection of anomalies. In real-
world use cases, two problems must be addressed: anomalous data is sparse and the same …
world use cases, two problems must be addressed: anomalous data is sparse and the same …
COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection
Existing approaches towards anomaly detection (AD) often rely on a substantial amount of
anomaly-free data to train representation and density models. However, large anomaly-free …
anomaly-free data to train representation and density models. However, large anomaly-free …
Low-Shot Unsupervised Visual Anomaly Detection via Sparse Feature Representation
Visual anomaly detection is an essential component in modern industrial manufacturing.
Existing studies using notions of pairwise similarity distance between a test feature and …
Existing studies using notions of pairwise similarity distance between a test feature and …
Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel Anomaly Detection
Anomaly detection (AD) is a machine learning task that identifies anomalies by learning
patterns from normal training data. In many real-world scenarios, anomalies vary in severity …
patterns from normal training data. In many real-world scenarios, anomalies vary in severity …
A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Anomaly Detection
In the advancement of industrial informatization, Unsupervised Industrial Anomaly Detection
(UIAD) technology effectively overcomes the scarcity of abnormal samples and significantly …
(UIAD) technology effectively overcomes the scarcity of abnormal samples and significantly …
Lara: A light and anti-overfitting retraining approach for unsupervised anomaly detection
Most of current anomaly detection models assume that the normal pattern remains same all
the time. However, the normal patterns of Web services change dramatically and frequently …
the time. However, the normal patterns of Web services change dramatically and frequently …
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection
Most of current anomaly detection models assume that the normal pattern remains the same
all the time. However, the normal patterns of web services can change dramatically and …
all the time. However, the normal patterns of web services can change dramatically and …