Anomaly Detection using Reconstruction Error: The essence of using autoencoders for anomaly detection lies in the computation of the reconstruction error. The reconstruction error is...

Jun 16, 2017 · When I am encoding and decoding over the test set, how do I calculate the reconstruction error (i.e. the Mean Squared Error/Loss) for each sample? In other words I'd like to see how well the Autoencoder is able to reconstruct its input so that I can use the Autoencoder as a single-class classifier.

Dec 19, 2024 · Reconstruction errors are the gaps between an autoencoder's output and the original input data. These errors occur when the model struggles to capture certain features during the encoding...

Jan 4, 2023 · In this paper, we propose a new approach for anomaly detection based on autoencoders. We assume vector instead of single value and consider reconstruction error and threshold for every feature. In fact, instead of using the summation of reconstruction error in one value, we create a vector of the reconstruction error.

To model normal behaviour we train the autoencoder on a normal data sample. This way, the model learns a mapping function that successfully reconstructs normal data samples with a very small...

Feb 17, 2021 · Autoencoders are used for unsupervised anomaly detection by first learning the features of the data set with mainly "normal" data points. Then new data can be considered anomalous if the new data has a large reconstruction error, i.e. it was hard to fit the features as in the normal data.

Oct 14, 2024 · I have an autoencoder based on a neural network. This model was trained using SCADA data. I got decent results in anomaly detection, with around 85% in the main metrics (recall, accuracy, precision, and F1 score).

Jan 10, 2025 · Autoencoders are great for anomaly detection because they can learn to capture the normal patterns in data. When they encounter something unusual, they struggle to reconstruct it accurately. This reconstruction error can be used as a signal for anomalies. Pretty neat, huh?

We have proposed a new approach by examining an autoencoder’s anomaly detection method based on data reconstruction error. Unlike the existing autoencoder-based anomaly detection techniques that consider the reconstruction error of all input features as a single value, we assume that the reconstruction error is a vector.

Jul 28, 2019 · Considering the reconstruction error of autoencoder can reflect the characteristic of anomalies, this paper presents a novel hyperpsectral anomaly detection alg