Multi-Class Anomaly Detection
Not on the map. Why?
Abstract (arXiv)
We study anomaly detection for the case when the normal class consists of more than one object category. This is an obvious generalization of the standard one-class anomaly detection problem. However, we show that jointly using multiple one-class anomaly detectors to solve this problem yields poorer results as compared to training a single one-class anomaly detector on all normal object categories together. We further develop a new anomaly detector called DeepMAD that learns compact distinguishing features by exploiting the multiple normal objects categories. This algorithm achieves higher AUC values for different datasets compared to two top performing one-class algorithms that either are trained on each normal object category or jointly trained on all normal object categories combined. In addition to theoretical results we present empirical results using the CIFAR-10, fMNIST, CIFAR-100, and a new dataset we developed called RECYCLE.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 27 of its 36 references that are in the map. First posted on arXiv 28 Oct 2021.
Builds on
27 of 36 references.
References · 27
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
- 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
- 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
- 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
- 2018Deep Anomaly Detection with Outlier Exposurecited by 942
- 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
- 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 514
- 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 453
- 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
- 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
- 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
- 2017Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samplescited by 389
15 more
- 2019Deep Semi-Supervised Anomaly Detectioncited by 291
- 2018Deep Anomaly Detection Using Geometric Transformationscited by 269
- 2018Learning Confidence for Out-of-Distribution Detection in Neural Networkscited by 245
- 2020Classification-Based Anomaly Detection for General Datacited by 211
- 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
- 2019OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representationscited by 199
- 2020Learning and Evaluating Representations for Deep One-class Classificationcited by 133
- 2018Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifierscited by 126
- 2020DROCC: Deep Robust One-Class Classificationcited by 121
- 2020Hybrid Models for Open Set Recognitioncited by 109
- 2018Generative Probabilistic Novelty Detection with Adversarial Autoencoderscited by 99
- 2018Anomaly Detection with Generative Adversarial Networks for Multivariate Time Seriescited by 55
- 2019Detecting semantic anomaliescited by 50
- 2019Deep Transfer Learning for Multiple Class Novelty Detectioncited by 33
- 2018Metric Learning for Novelty and Anomaly Detectioncited by 21
Built on by
Citing papers per year: 2025: 2; 2026: 1.
3 papers.