Papers One-Class Classification
“One-Class Classification” 태그가 달린 논문 227편 · 필터 해제
Learning Off-Road Terrain Traversability with Self-Supervisions Only
Estimating the traversability of terrain should be reliable and accurate in diverse conditions for autonomous driving in off-road environments. However, learning-based approaches often yield unreliable results when confr…
Autonomous DrivingOne-Class ClassificationSelf-Supervised LearningWooden Sleeper Deterioration Detection for Rural Railway Prognostics Using Unsupervised Deeper FCDDs
Maintaining high standards for user safety during daily railway operations is crucial for railway managers. To aid in this endeavor, top- or side-view cameras and GPS positioning systems have facilitated progress toward …
One-Class ClassificationSLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification
Industrial image anomaly detection under the setting of one-class classification has significant practical value. However, most existing models struggle to extract separable feature representations when performing featur…
Anomaly DetectionClassificationOne-Class ClassificationSelf-Supervised LearningHyperspectral Image Analysis with Subspace Learning-based One-Class Classification
Hyperspectral image (HSI) classification is an important task in many applications, such as environmental monitoring, medical imaging, and land use/land cover (LULC) classification. Due to the significant amount of spect…
ClassificationDimensionality Reductionfeature selectionHyperspectral image analysis+1Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection
Time series anomaly detection (TSAD) plays a vital role in many industrial applications. While contrastive learning has gained momentum in the time series domain for its prowess in extracting meaningful representations f…
Anomaly DetectionContrastive LearningOne-Class ClassificationSelf-Supervised Learning+2PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies
Due to the rarity of anomalous events, video anomaly detection is typically approached as one-class classification (OCC) problem. Typically in OCC, an autoencoder (AE) is trained to reconstruct the normal only training d…
Anomaly DetectionOne-Class ClassificationVideo Anomaly DetectionDiversity-Measurable Anomaly Detection
Reconstruction-based anomaly detection models achieve their purpose by suppressing the generalization ability for anomaly. However, diverse normal patterns are consequently not well reconstructed as well. Although some e…
Anomaly DetectionAnomaly Detection In Surveillance VideosDefect DetectionDiversity+1Synthetic Pseudo Anomalies for Unsupervised Video Anomaly Detection: A Simple yet Efficient Framework based on Masked Autoencoder
Due to the limited availability of anomalous samples for training, video anomaly detection is commonly viewed as a one-class classification problem. Many prevalent methods investigate the reconstruction difference produc…
Anomaly DetectionOne-Class ClassificationVideo Anomaly DetectionUnsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics
One-class classification has been a prevailing method in building deep anomaly detection models under the assumption that a dataset consisting of normal samples is available. In practice, however, abnormal samples are of…
Anomaly DetectionOne-Class ClassificationOutlier DetectionAnomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors
Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery is aimed at segmenting anomaly patterns of the earth deviating from normal patterns, which plays an important role in various Earth vision appl…
Anomaly SegmentationOne-Class ClassificationSegmentationRobust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks
We propose a new method, dubbed One Class Signed Distance Function (OCSDF), to perform One Class Classification (OCC) by provably learning the Signed Distance Function (SDF) to the boundary of the support of any distribu…
Image GenerationOne-Class ClassificationAdapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation
We propose an incremental improvement to Fully Convolutional Data Description (FCDD), an adaptation of the one-class classification approach from anomaly detection to image anomaly segmentation (a.k.a. anomaly localizati…
Anomaly DetectionAnomaly LocalizationAnomaly SegmentationOne-Class ClassificationInter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification
Unsupervised anomaly detection and localization in images is a challenging problem, leading previous methods to attempt an easier supervised one-class classification formalization. Assuming training images to be real…
Anomaly DetectionOne-Class ClassificationUnsupervised Anomaly DetectionQuery by example in remote sensing image archive using enhanced deep support vector data description
This article studies remote sensing image retrieval using kernel-based support vector data description (SVDD). We exploit deep SVDD, which is a well-known method for one-class classification to recover the most relevant …
Image RetrievalOne-Class ClassificationLOSDD: Leave-Out Support Vector Data Description for Outlier Detection
Support Vector Machines have been successfully used for one-class classification (OCSVM, SVDD) when trained on clean data, but they work much worse on dirty data: outliers present in the training data tend to become supp…
One-Class ClassificationOutlier DetectionAn Upper Bound for the Distribution Overlap Index and Its Applications
This paper proposes an easy-to-compute upper bound for the overlap index between two probability distributions without requiring any knowledge of the distribution models. The computation of our bound is time-efficient an…
ClassificationOne-Class ClassificationOne-class classifierChaotic Variational Auto Encoder based One Class Classifier for Insurance Fraud Detection
Of late, insurance fraud detection has assumed immense significance owing to the huge financial & reputational losses fraud entails and the phenomenal success of the fraud detection techniques. Insurance is majorly divid…
Fraud DetectionOne-Class ClassificationOne-class classifierDOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection
Machine Learning (ML) approaches have been used to enhance the detection capabilities of Network Intrusion Detection Systems (NIDSs). Recent work has achieved near-perfect performance by following binary- and multi-class…
Anomaly DetectionIntrusion DetectionNetwork Intrusion DetectionOne-Class Classification+2DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection
Visual anomaly detection, an important problem in computer vision, is usually formulated as a one-class classification and segmentation task. The student-teacher (S-T) framework has proved to be effective in solving this…
Anomaly DetectionDecoderDenoisingOne-Class Classification+1Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection
Gaining the trust of customers and providing them empathy are very critical in the financial domain. Frequent occurrence of fraudulent activities affects these two factors. Hence, financial organizations and banks must t…
Binary ClassificationCausal InferenceExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)+2