In some scenarios, classifier requires detecting out-of-distribution samples far from its training data. With desirable characteristics, reconstruction autoenco ...

Figure 5B shows the distribution of reconstruction errors using different detection algorithms. The autoencoder had the lowest overlap between the reconstruction error distributions for abnormal and ...

For an autoencoder anomaly detection system, model overfitting is characterized by a situation where all reconstructed inputs match the source inputs very closely, and therefore all reconstruction ...

Anomaly Detection and clarification using Autoencoder and Shap Values An autoencoder can be used to detect anomalies through the reconstruction error (anomaly score).

Reconstruction-based anomaly detection with encoders can be applied in a variety of real-world scenarios, including: ...

We propose an unsupervised method for detecting adversarial attacks in inner layers of autoencoder (AE) networks by maximizing a non-parametric measure of anomalous node activations.