Linganisha mbinu

	Uchanganuzi wa Anomali kwa Kutumia Autoencoder Mtandaoni	Ugunduzi wa Anomaly kwa Kutumia Autoencoder za Nusu-Msimamizi
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2010s–present	2018–2020
Mwanzilishi	Various (online/incremental deep learning community)	Ruff, L. et al.; Zong, B. et al.
Aina	Online unsupervised anomaly detection	Semi-supervised deep anomaly detection
Chanzo asilia	An, J. & Cho, S. (2015). Variational Autoencoder based Anomaly Detection using Reconstruction Probability. SNU Data Mining Center, 2015-2.	Ruff, L., Vandermeulen, R. A., Franks, B. J., Müller, K.-R., & Kloft, M. (2020). Deep Semi-Supervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2020).
Majina mbadala	incremental autoencoder anomaly detection, streaming autoencoder anomaly detection, online AE anomaly detection, continual autoencoder anomaly detection	Semi-supervised AE anomaly detection, SSAD autoencoder, semi-supervised reconstruction-error detection, partially labeled autoencoder anomaly detection
Zinazohusiana	5	5
Muhtasari	Online Autoencoder Anomaly Detection trains an autoencoder incrementally on a continuous data stream, flagging observations whose reconstruction error exceeds an adaptive threshold as anomalies. This approach combines the representational power of deep autoencoders with the incremental update capability of online learning, making it suitable for real-time or high-volume streaming scenarios where batch retraining is impractical.	Semi-supervised Autoencoder Anomaly Detection trains a neural autoencoder primarily on normal (unlabeled) data, then uses a small set of labeled anomalies to refine decision boundaries, detecting anomalies as samples with high reconstruction error. It bridges the gap between purely unsupervised autoencoders and fully supervised classifiers when labels are scarce but some known anomalies exist.