One-Class Classification

61 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.

Most implemented papers

Learning Deep Features for One-Class Classification

PramuPerera/DeepOneClass 16 Jan 2018

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 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 24 Jan 2019

We present a novel Convolutional Neural Network (CNN) based approach for one class classification.

Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection

ristea/sspcab CVPR 2022

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

MoonBlvd/tad-IROS2019 2 Mar 2019

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

WangXuhongCN/OCGNN 22 Feb 2020

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

liznerski/fcdd ICLR 2021

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 18 Sep 2020

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 25 Jan 2021

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

ngoix/ocrf 7 Nov 2016

Random Forests (RFs) are strong machine learning tools for classification and regression.