Sep 26, 2023 · Setting an appropriate threshold for the reconstruction error is crucial to the effectiveness of the anomaly detection system. This threshold is often determined during the …

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 …

May 8, 2023 · By controlling the reconstruction error, you ensure that the encoder reduces the dimensions while giving importance to features that contain more informative content. For …

Apr 20, 2025 · To set the reconstruction error threshold, you need to follow these steps: Calculate the reconstruction error: Calculate the reconstruction error for each data point in the testing …

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 …

Dec 9, 2024 · During training, the Autoencoder learns to minimize the reconstruction error, which encourages it to learn the underlying structure of the data. Anomalies are then detected as …

Jan 4, 2023 · The difference between the original input and the reconstruction output in the autoencoder is called the reconstruction error. An autoencoder-based anomaly detection …

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 …

Autoencoders is to ignore anomalies and minimize the reconstruction error on normal data. The goal of this work is to investigate approaches to allow reconstruction error-based architectures …

Jan 10, 2025 · Step 4: Calculate Reconstruction Error. Once your autoencoder is trained, you can use it to calculate the reconstruction error on new data. This error can be used as an anomaly …