# 異常検出

* [DFKDE](aiml_cv_anomaly_detection)
  * Classification型
  * Deep Learningで特徴量抽出を行う
  * その後PCAとKernel Density Estimationで異常分類をするモデル
  * 高速な異常検知が特徴
* [GANomaly (2018-05-17)](aiml_cv_anomaly_detection) / [arXiv](https://arxiv.org/abs/1805.06725) / [Papers With Code](https://paperswithcode.com/paper/ganomaly-semi-supervised-anomaly-detection)
  * Classification型
* [DFM (2019-09-25)](aiml_cv_anomaly_detection) / [arXiv](https://arxiv.org/abs/1909.11786) / [Papers With Code](https://paperswithcode.com/paper/probabilistic-modeling-of-deep-features-for)
  * Classification型
* [PaDiM (2020-11-17)](aiml_cv_anomaly_detection) / [arXiv](https://arxiv.org/abs/2011.08785) / [Papers With Code](https://paperswithcode.com/paper/padim-a-patch-distribution-modeling-framework)
* [STFPM (2021-03-07)](aiml_cv_anomaly_detection) / [arXiv](https://arxiv.org/abs/2103.04257) / [Papers With Code](https://paperswithcode.com/paper/student-teacher-feature-pyramid-matching-for)
* [PatchCore (2021-06-15)](aiml_cv_anomaly_detection) / [arXiv](https://arxiv.org/abs/2106.08265) / [Papers With Code](https://paperswithcode.com/paper/towards-total-recall-in-industrial-anomaly)
* [CFLOW-AD (2021-07-27)](aiml_cv_anomaly_detection) / [arXiv](https://arxiv.org/abs/2107.12571) / [Papers With Code](https://paperswithcode.com/paper/cflow-ad-real-time-unsupervised-anomaly)
* [FastFlow (2021-11-15)](aiml_cv_anomaly_detection) / [arXiv](https://arxiv.org/abs/2111.07677) / [Papers With Code](https://paperswithcode.com/paper/fastflow-unsupervised-anomaly-detection-and)
