Practical AI Solution: Enhancing Anomaly Detection with Adaptive Noise
Value and Practical Solutions
Anomaly detection is crucial in surveillance, medical analysis, and network security. Our approach introduces a robust method to improve anomaly detection by training an autoencoder to reconstruct normal input well while poorly reconstructing anomalies. This is achieved by incorporating learned adaptive noise into the normal data, creating pseudo anomalies that are reconstructed poorly by the autoencoder. The method offers efficient inference during testing without additional computational cost.
Evaluation and Applicability
We have evaluated our method across diverse datasets, including surveillance videos, CIFAR-10 images, and the KDDCUP99 network intrusion dataset. Our approach has demonstrated its superiority and generic applicability in video, image, and network intrusion domains. It has been extensively compared with baselines and state-of-the-art methods, showcasing its effectiveness.
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