Supervised Anomaly Detection
50 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.
Libraries
Use these libraries to find Supervised Anomaly Detection models and implementations
MVTecAD
BTAD
CHAD
Most implemented papers
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
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
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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
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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.
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization
hmsch/natural-synthetic-anomalies
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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.
How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?
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
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We consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale unlabeled dataset.
Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning
In addition to using the conditional GAN to generate class balanced supplementary training data, an innovative ensemble learning loss function ensuring each discriminator makes up for the deficiencies of the others is designed to overcome the class imbalanced problem, and an active learning algorithm is introduced to significantly reduce the cost of labeling real-world data.
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection
xuhongzuo/DeepOD
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Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data.
Diffusion Models for Medical Anomaly Detection
cian.unibas.ch/diffusion-anomaly
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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
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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).
