One-class classifier
24 papers with code • 0 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in One-class classifier
COVID-19-CT-CXR
Simulated micro-Doppler Signatures
Latest papers
ARCADe: A Rapid Continual Anomaly Detector
AhmedFrikha/ARCADe-A-Rapid-Continual-Anomaly-Detector
•
•
Although continual learning and anomaly detection have separately been well-studied in previous works, their intersection remains rather unexplored.
Quantum One-class Classification With a Distance-based Classifier
We present a new classifier based on HC named Quantum One-class Classifier (QOCC) that consists of a minimal quantum machine learning model with fewer operations and qubits, thus being able to mitigate errors from NISQ (Noisy Intermediate-Scale Quantum) computers.
Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks
The proposed system is evaluated on the publicly available WMCA multi-channel face PAD database, which contains a wide variety of 2D and 3D attacks.
Few-Shot One-Class Classification via Meta-Learning
AhmedFrikha/Few-Shot-One-Class-Classification-via-Meta-Learning
•
•
Our experiments on eight datasets from the image and time-series domains show that our method leads to better results than classical OCC and few-shot classification approaches, and demonstrate the ability to learn unseen tasks from only few normal class samples.
COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature
(1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved DL performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza and trained a DL baseline to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We trained an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR.
Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm
xaggi/OGNet
•
•
CVPR 2020
Another possible approach is to use both generator and discriminator for anomaly detection.
Dynamic Decision Boundary for One-class Classifiers applied to non-uniformly Sampled Data
A typical issue in Pattern Recognition is the non-uniformly sampled data, which modifies the general performance and capability of machine learning algorithms to make accurate predictions.
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output
vahdat-ab/oodl
•
•
Several approaches have been proposed to detect OOD inputs, but the detection task is still an ongoing challenge.
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring Signals
The performance is first evaluated on a synthetic dataset that encompasses typical characteristics of condition monitoring data.
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
podgorskiy/GPND
•
•
NeurIPS 2018
We assume that training data is available to describe only the inlier distribution.
