One-class classifier
24 papers with code • 0 benchmarks • 3 datasets
Benchmarks
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COVID-19-CT-CXR
Simulated micro-Doppler Signatures
Latest papers with no code
Chaotic Variational Auto Encoder based One Class Classifier for Insurance Fraud Detection
The effectiveness of C-VAE is demonstrated on the health insurance fraud and auto insurance datasets.
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection
Therefore, this paper proposes a Deep One-Class (DOC) classifier for network intrusion detection by only training on benign network data samples.
Cutting Through the Noise: An Empirical Comparison of Psychoacoustic and Envelope-based Features for Machinery Fault Detection
We train a state-of-the-art one-class-classifier, on samples from healthy motors and separate the faulty ones for fault detection using a threshold.
One-Class Risk Estimation for One-Class Hyperspectral Image Classification
Hyperspectral imagery (HSI) one-class classification is aimed at identifying a single target class from the HSI by using only knowing positive data, which can significantly reduce the requirements for annotation.
OCFormer: One-Class Transformer Network for Image Classification
We propose a novel deep learning framework based on Vision Transformers (ViT) for one-class classification.
Multi-class versus One-class classifier in spontaneous speech analysis oriented to Alzheimer Disease diagnosis
Most of medical developments require the ability to identify samples that are anomalous with respect to a target group or control group, in the sense they could belong to a new, previously unseen class or are not class data.
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection
The network intrusion detection task is challenging because of the imbalanced and unlabeled nature of the dataset it operates on.
Flow-based SVDD for anomaly detection
We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools.
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection
We demonstrate our method on various unsupervised AD tasks with image and tabular data.
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection
The Radial Basis Function Data Descriptor (RBFDD) network is an effective solution for anomaly detection, however, it is a shallow model that does not deal effectively with raw data representations.
