Papers One-Class Classification
“One-Class Classification” 태그가 달린 논문 227편 · 필터 해제
SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images
Medical anomaly detection (AD) is crucial for early clinical intervention, yet it faces challenges due to limited access to high-quality medical imaging data, caused by privacy concerns and data silos. Few-shot learning …
Anomaly DetectionFew-Shot LearningLarge Language ModelOne-Class ClassificationAdversarial Subspace Generation for Outlier Detection in High-Dimensional Data
Outlier detection in high-dimensional tabular data is challenging since data is often distributed across multiple lower-dimensional subspaces -- a phenomenon known as the Multiple Views effect (MV). This effect led to a …
feature selectionOne-Class ClassificationOutlier DetectionStochastic OptimizationRoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data
The accumulation of time-series signals and the absence of labels make time-series Anomaly Detection (AD) a self-supervised task of deep learning. Methods based on normality assumptions face the following three limitatio…
Anomaly DetectionContrastive LearningOne-Class ClassificationTime Series+1From Pixels to Trajectory: Universal Adversarial Example Detection via Temporal Imprints
For the first time, we unveil discernible temporal (or historical) trajectory imprints resulting from adversarial example (AE) attacks. Standing in contrast to existing studies all focusing on spatial (or static) imprint…
One-Class ClassificationFedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection
Unsupervised image anomaly detection (UAD) has become a critical process in industrial and medical applications, but it faces growing challenges due to increasing concerns over data privacy. The limited class diversity i…
Anomaly DetectionFederated LearningOne-Class ClassificationAnomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach
In this paper, we address an anomaly detection problem in smart power grids using Multimodal Subspace Support Vector Data Description (MS-SVDD). This approach aims to leverage better feature relations by considering the …
Anomaly DetectionEvent DetectionOne-Class ClassificationOne Class Restricted Kernel Machines
Restricted kernel machines (RKMs) have demonstrated a significant impact in enhancing generalization ability in the field of machine learning. Recent studies have introduced various methods within the RKM framework, comb…
One-Class ClassificationTeacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection
Visual anomaly detection is a highly challenging task, often categorized as a one-class classification and segmentation problem. Recent studies have demonstrated that the student-teacher (S-T) framework effectively addre…
Anomaly DetectionAnomaly SegmentationDecoderDenoising+2Score Combining for Contrastive OOD Detection
In out-of-distribution (OOD) detection, one is asked to classify whether a test sample comes from a known inlier distribution or not. We focus on the case where the inlier distribution is defined by a training dataset an…
Anomaly DetectionContrastive LearningNovelty DetectionOne-Class Classification+2On the Adversarial Robustness of Benjamini Hochberg
The Benjamini-Hochberg (BH) procedure is widely used to control the false detection rate (FDR) in multiple testing. Applications of this control abound in drug discovery, forensics, anomaly detection, and, in particular,…
Adversarial RobustnessAnomaly DetectionDrug DiscoveryOne-Class Classification+2Task-Specific Gradient Adaptation for Few-Shot One-Class Classification
Optimization-based meta-learning methods for few-shot one-class classification (FS-OCC) aim to fine-tune a meta-trained model to classify the positive and negative samples using only a few positive samples by adaptat…
Meta-LearningOne-Class ClassificationBeyond Generation: A Diffusion-based Low-level Feature Extractor for Detecting AI-generated Images
The prevalence of AI-generated images has evoked concerns regarding the potential misuse of image generation technologies. In response, numerous detection methods aim to identify AI-generated images by analyzing gene…
DenoisingImage GenerationOne-Class ClassificationMEATRD: Multimodal Anomalous Tissue Region Detection Enhanced with Spatial Transcriptomics
The detection of anomalous tissue regions (ATRs) within affected tissues is crucial in clinical diagnosis and pathological studies. Conventional automated ATR detection methods, primarily based on histology images alone,…
One-Class ClassificationFUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data
While the mainstream research in anomaly detection has mainly followed the one-class classification, practical industrial environments often incur noisy training data due to annotation errors or lack of labels for new or…
Anomaly DetectionOne-Class ClassificationUnsupervised Anomaly DetectionDisentangling Tabular Data Towards Better One-Class Anomaly Detection
Tabular anomaly detection under the one-class classification setting poses a significant challenge, as it involves accurately conceptualizing "normal" derived exclusively from a single category to discern anomalies from …
Anomaly DetectionDisentanglementOne-Class ClassificationPoint Cloud Novelty Detection Based on Latent Representations of a General Feature Extractor
We propose an effective unsupervised 3D point cloud novelty detection approach, leveraging a general point cloud feature extractor and a one-class classifier. The general feature extractor consists of a graph-based autoe…
Novelty DetectionOne-Class ClassificationOne-class classifierOn The Relationship between Visual Anomaly-free and Anomalous Representations
Anomaly Detection is an important problem within computer vision, having variety of real-life applications. Yet, the current set of solutions to this problem entail known, systematic shortcomings. Specifically, contempor…
Anomaly DetectionDomain AdaptationFew-Shot LearningOne-Class Classification+1Linear-time One-Class Classification with Repeated Element-wise Folding
This paper proposes an easy-to-use method for one-class classification: Repeated Element-wise Folding (REF). The algorithm consists of repeatedly standardizing and applying an element-wise folding operation on the one-cl…
ClassificationOne-Class ClassificationNon-Robust Features are Not Always Useful in One-Class Classification
The robustness of machine learning models has been questioned by the existence of adversarial examples. We examine the threat of adversarial examples in practical applications that require lightweight models for one-clas…
Multi-class ClassificationOne-Class ClassificationQuality assurance of organs-at-risk delineation in radiotherapy
The delineation of tumor target and organs-at-risk is critical in the radiotherapy treatment planning. Automatic segmentation can be used to reduce the physician workload and improve the consistency. However, the quality…
One-Class ClassificationOne-class classifierSpecificity