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NDNET: A Unified Framework for Anomaly and Novelty Detection
We introduce NDNET ( https://novelty-detection.net/p/ndnet ), an anomaly and novelty detection library...
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Novelty and Emotion in Misinformation Detection
The third chapter investigates how and why novelty and emotion are the key attributes for detection of misinformation and prevention of its spread....
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Introduction to Anomaly Detection
In this chapter, you will learn about anomalies in general, the categories of anomalies, and anomaly detection. You will also learn why anomaly...
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Review on novelty detection in the non-stationary environment
Novelty detection and concept drift detection are essential for the plethora of machine learning applications. The statistical properties of...
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Novelty Detection-Based Automated Anomaly Identification via Optimized Deep Generative Model
Novelty detection (ND) is a crucial task in machine learning to identify anomalies in the test data in some respects different from the training...
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Contextual and Semantic Novelty in Text
Novelty, anomaly, or out-of-distribution (OOD) detection has been an active research area for decades, since the 1960s [1, 2], due to its widespread...
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MÆIDM: multi-scale anomaly embedding inpainting and discrimination for surface anomaly detection
The detection of anomalous structures in natural image data plays a crucial role in numerous tasks in the field of computer vision. Methods based on...
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Multi-Class Anomaly Detection
We study anomaly detection for the case when the normal class consists of more than one object category. This is an obvious generalization of the...
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Anomaly Detection in Smart Houses for Healthcare
Nowadays, device monitoring is an activity present in various different environments. Ranging from monitoring workers in their workplaces, city...
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Toward Unsupervised Energy Consumption Anomaly Detection
Existing high-performance Machine Learning models typically rely on large training datasets with high-quality manual annotations, which are difficult...
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Improving autoencoder by mutual information maximization and shuffle attention for novelty detection
Under an open dynamic environment, a challenging task in object detection is to determine whether samples belong to a known class. Novelty detection...
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Explainable Anomaly Detection in Industrial Streams
Anomaly detection in industrial environment is a complex task, which requires to consider multiple characteristics of the data from industrial...
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Synergistic fusion of wavelet and superpixels for complementary hyperspectral anomaly detection
Hyperspectral imaging enables discrimination of materials based on rich spectral signatures. This makes it well-suited for anomaly detection. A novel...
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The Difficulty of Novelty Detection and Adaptation in Physical Environments
Detecting and adapting to novel situations is a major challenge for AI systems that operate in open-world environments. One reason for this challenge...
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Context discovery for anomaly detection
Contextual anomaly detection aims to identify objects that are anomalous only within specific contexts, while appearing normal otherwise. However,...
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FRAnomaly: flow-based rapid anomaly detection from images
AbstractDetecting anomalies, such as defects in newly manufactured products or damage in long-used material structures, is a tedious task for humans....
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MC-MIL: video surveillance anomaly detection with multi-instance learning and multiple overlapped cameras
Anomaly detection approaches have limiting aspects regarding the representativeness of the information since the video data is captured from a single...
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Anomaly detection for image data based on data distribution and reconstruction
Anomaly detection is a classical problem of identifying whether a query is an inlier or an outlier, with only inliers available during training....
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CLOE: Novelty Detection via Contrastive Learning with Outlier Exposure
Novelty detection (ND) methods seek to identify anomalies within a specific dataset. Although self-supervised representation learning is commonly...
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That’s BAD: blind anomaly detection by implicit local feature clustering
Recent studies on visual anomaly detection (AD) of industrial objects/textures have achieved quite good performance. They consider an unsupervised...
