Supervised Anomaly Detection

41 papers with code • 2 benchmarks • 3 datasets

In the training set, the amount of abnormal samples is limited and significant fewer than normal samples, producing data distributions that lead to a naturally imbalanced learning problem.


Use these libraries to find Supervised Anomaly Detection models and implementations
3 papers

Most implemented papers

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

openvinotoolkit/anomalib 17 May 2018

Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal).

Deep Semi-Supervised Anomaly Detection

lukasruff/Deep-SAD-PyTorch ICLR 2020

Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.

Deep Weakly-supervised Anomaly Detection

mala-lab/prenet 30 Oct 2019

To detect both seen and unseen anomalies, we introduce a novel deep weakly-supervised approach, namely Pairwise Relation prediction Network (PReNet), that learns pairwise relation features and anomaly scores by predicting the relation of any two randomly sampled training instances, in which the pairwise relation can be anomaly-anomaly, anomaly-unlabeled, or unlabeled-unlabeled.

How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?

ngoix/EMMV_benchmarks 5 Jul 2016

When sufficient labeled data are available, classical criteria based on Receiver Operating Characteristic (ROC) or Precision-Recall (PR) curves can be used to compare the performance of un-supervised anomaly detection algorithms.

Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data

lflfdxfn/DPLAN-Implementation 15 Sep 2020

We consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale unlabeled dataset.

Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection

xuhongzuo/DeepOD 22 May 2021

Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data.

Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization

hmsch/natural-synthetic-anomalies 30 Sep 2021

We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies (NSA), for training an end-to-end model for anomaly detection and localization using only normal training data.

Diffusion Models for Medical Anomaly Detection 8 Mar 2022

In medical applications, weakly supervised anomaly detection methods are of great interest, as only image-level annotations are required for training.

Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks

tbw162/purigan 3 Feb 2023

When training on such datasets, existing GANs will learn a mixture distribution of desired and contaminated instances, rather than the desired distribution of desired data only (target distribution).

Weakly Supervised Anomaly Detection: A Survey

yzhao062/wsad 9 Feb 2023

Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.