One-Class Classification
60 papers with code • 0 benchmarks • 0 datasets
One-class classification (OCC) algorithms serve a crucial role in scenarios where the negative class is either absent, poorly sampled, or not well defined. This unique situation presents a challenge for building effective classifiers, as they must delineate the class boundary solely based on knowledge of the positive class. OCC has found application in various research domains, including outlier/novelty detection and concept learning.
In the context of anomaly detection, OCC models are trained exclusively on "normal" data and are subsequently tasked with identifying anomalous patterns during inference.
A one-class classifier aims at capturing characteristics of training instances, in order to be able to distinguish between them and potential outliers to appear.
— Page 139, Learning from Imbalanced Data Sets, 2018.
Benchmarks
These leaderboards are used to track progress in One-Class Classification
Most implemented papers
Learning Deep Features for One-Class Classification
We propose a deep learning-based solution for the problem of feature learning in one-class classification.
Adversarially Learned One-Class Classifier for Novelty Detection
khalooei/ALOCC-CVPR2018
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CVPR 2018
Our architecture is composed of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples.
One-Class Convolutional Neural Network
otkupjnoz/oc-cnn
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We present a novel Convolutional Neural Network (CNN) based approach for one class classification.
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field.
Unsupervised Traffic Accident Detection in First-Person Videos
Recognizing abnormal events such as traffic violations and accidents in natural driving scenes is essential for successful autonomous driving and advanced driver assistance systems.
One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks
Since traditional anomaly detection methods are stable, robust and easy to use, it is vitally important to generalize them to graph data.
Explainable Deep One-Class Classification
Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away.
Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change
ctyeong/IO-GEN
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This method can be used to screen video frames for which additional human observation is needed.
Deep One-Class Classification via Interpolated Gaussian Descriptor
tianyu0207/IGD
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The adversarial interpolation is enforced to consistently learn a smooth Gaussian descriptor, even when the training data is small or contaminated with anomalous samples.
One Class Splitting Criteria for Random Forests
Random Forests (RFs) are strong machine learning tools for classification and regression.
