There are 11 repositories under one-class-learning topic.
ThunderSVM: A Fast SVM Library on GPUs and CPUs
A PyTorch implementation of the Deep SVDD anomaly detection method
A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.
A PyTorch implementation of Context Vector Data Description (CVDD), a method for Anomaly Detection on text.
Repository for the paper "Rethinking Assumptions in Anomaly Detection"
Pytorch implementation of code used for one-class anomaly detection based face anti-spoofing
Official implementation of NeurIPS'23 paper "Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection"
Deep One-Class Classification using Intra-Class Splitting
Prior Generating Networks for Anomaly Detection
Anomaly IDS using a one-class autoencoder.
A pill quality control dataset and associated anomaly detection example
Package provides the direct java conversion of the origin libsvm C codes as well as a number of adapter to make it easier to program with libsvm on Java
CLEAR: Cumulative LEARning for one-shot one-class image recognition (CVPR 2018)
Subspace Support Vector Data Description
One-class classification approach using error of image transformation into one image
Anomaly detection for deep SVDD
This repository contains all the Deep Learning projects that I have developed/worked in the areas of Natural Language Processing and Computer Vision by using the deep learning frameworks such as tensorflow, opencv, keras, spacy and pytorch.
Python library for one-class nu-SVM algorithm with "privileged information", compatible with scikit-learn
One-class classification algorithm for univariate timeseries data
Deep SA-SVDD for Anomaly detection
Novelty Detection
One-Class Classification Ensembles with Unsupervised Representations to Detect Novelty
Most existing classification approaches assume the underlying training set is evenly distributed but many real-world classification problems have an imbalanced class distribution, such as rare disease identification, fraud detection, spam detection, churn prediction, electricity theft & pilferage etc.
Fraud hosts with substantial amount of fraudulent traffic using the impression logs for selected IP addresses