Linganisha mbinu

	Ugunduzi Imara wa Hitilafu kwa Kutumia Autoencoder	Isolation Forest Imara
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2017	2008–2019
Mwanzilishi	Zhou, C. & Paffenroth, R. C.	Liu, F. T., Ting, K. M., Zhou, Z.-H. (base); robust extensions by multiple authors
Aina	Unsupervised anomaly detection (robust deep learning)	Robust ensemble anomaly detection
Chanzo asilia	Zhou, C., & Paffenroth, R. C. (2017). Anomaly detection with robust deep autoencoders. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 665–674). ACM.	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 413–422. IEEE.
Majina mbadala	Robust Deep Autoencoder, Robust AE Anomaly Detection, RDAE, Robust Reconstruction-Based Anomaly Detection	Robust iForest, noise-robust isolation forest, contamination-robust isolation forest, robust anomaly isolation
Zinazohusiana	5	5
Muhtasari	Robust Autoencoder Anomaly Detection extends the standard autoencoder framework with robustness mechanisms — such as sparse decomposition, robust loss functions, or adversarial regularisation — so that the model learns a compact representation of normal behaviour while remaining resistant to the corrupting influence of anomalies embedded in the training data.	Robust Isolation Forest extends the classic Isolation Forest anomaly detector with strategies that reduce sensitivity to data contamination, masking effects, and biased random splits. By incorporating robustness mechanisms — such as improved subsampling, re-weighting of suspicious regions, or bias-corrected splitting — it achieves more reliable anomaly scores when the training data itself contains a non-trivial fraction of anomalies or when specific feature distributions cause standard iForest to produce unreliable path lengths.