Anomaly Detection in Neural Networks via One-Class Support Vector Methods

Music City Center 

Artificial Neural Networks (ANNs) make predictions based on patterns learned during training. However, their performance can decline if the input data distribution shifts over time, causing the model's assumptions to become invalid. To maintain reliable predictions, it is crucial to monitor for these distribution changes and update or retrain the model when necessary to adapt to new data patterns.

To address this issue, we propose utilizing one-class classification techniques to monitor the latent feature representations, or "embeddings," produced by the ANN. One-class classification methods can detect shifts in the data stream by identifying deviations from established boundaries within the feature space. If new data points begin to fall outside these boundaries, it indicates a potential change in the underlying data distribution or the parameters of the neural network, which may impact model accuracy and highlight the need for retraining. This approach is evaluated by applying LS-SVDD and SVDD to a publicly available dataset and comparing their performance.

Embedding Layer

ANN

One-class Classification

LS-SVDD

SVDD 

Section on Statistical Computing