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
Most implemented papers
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.
Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm
xaggi/OGNet
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CVPR 2020
Another possible approach is to use both generator and discriminator for anomaly detection.
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.
Few-Shot One-Class Classification via Meta-Learning
AhmedFrikha/Few-Shot-One-Class-Classification-via-Meta-Learning
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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.
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.
ARCADe: A Rapid Continual Anomaly Detector
AhmedFrikha/ARCADe-A-Rapid-Continual-Anomaly-Detector
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Although continual learning and anomaly detection have separately been well-studied in previous works, their intersection remains rather unexplored.
Learning and Evaluating Representations for Deep One-class Classification
google-research/deep_representation_one_class
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ICLR 2021
We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations.
Shell Theory: A Statistical Model of Reality
wen-yan-lin/shell-theory
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IEEE Transactions on Pattern Analysis and Machine Intelligence 2021
The foundational assumption of machine learning is that the data under consideration is separable into classes; while intuitively reasonable, separability constraints have proven remarkably difficult to formulate mathematically.
Exemplar-free Class Incremental Learning via Discriminative and Comparable One-class Classifiers
DisCOIL follows the basic principle of POC, but it adopts variational auto-encoders (VAE) instead of other well-established one-class classifiers (e. g. deep SVDD), because a trained VAE can not only identify the probability of an input sample belonging to a class but also generate pseudo samples of the class to assist in learning new tasks.
SIFT and SURF based feature extraction for the anomaly detection
In this paper, we suggest a way, how to use SIFT and SURF algorithms to extract the image features for anomaly detection.
