AnoRand - Deep Learning-Based Semi-Supervised Anomaly Detection with Synthetic Labels

Mansour ZOUBEIROU A MAYAKI, Michel Riveill

Keywords: Outlier detection, anomaly detection, unsupervised learning, semi-supervised learning

Abstract:

Anomaly detection, or more generally outlier detection, is one of the most popular and challenging topics in theoretical and applied machine learning. The main challenge is that in general we have access to very few labeled data or no labels at all.
In this paper, we present a new semi-supervised anomaly detection method called AnoRand by combining a deep learning architecture with random synthetic label generation.
The proposed architecture has two building blocks: (1) a noise detection (ND) block composed of feed forward perceptron and (2) an autoencoder (AE) block.
The main idea of this new architecture is to learn one class (e.g. the majority class in case of anomaly detection) as well as possible by taking advantage of the ability of auto encoders to represent data in a latent space and the ability of Feed Forward Perceptron (FFP) to learn one class when the data is highly imbalanced. First, we create synthetic anomalies by randomly disturbing (add noise) a few samples (e.g., 2%) from the training set. Second, we use the normal and synthetic samples as input to our model.
We compared the performance of the proposed method to 17 state-of-the-art unsupervised anomaly detection method on synthetic datasets and 57 real-world datasets.
Our results show that this new method generally outperforms most of the state-of-the-art methods and has the best performance (AUC ROC and AUC PR) on the vast majority of reference datasets.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 5003