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.

Aug 16, 2024 · If you examine the reconstruction error for the anomalous examples in the test set, you'll notice most have greater reconstruction error than the threshold. By varing the threshold, you can adjust the precision and recall of your classifier.

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.

Sep 26, 2023 · During production, each newly manufactured chip image is passed through the autoencoder. The model attempts to reconstruct the chip image, and the reconstruction error is calculated. If the...

Sep 16, 2019 · I have calculated the reconstruction error in the training set and in the test set for each point (calculated as the squared distance between the original and the reconstructed point), as well as the MSE in both sets of course.

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...

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...

Oct 14, 2024 · The "id" parameter contributed around 50-60% to the reconstruction error (RE), while the parameter that should be correlated with the anomaly contributed only 10% to the RE. The "other" parameter represents the sum of all parameters that are not "id". The graph below shows this behavior:

Figure 3 demonstrates the inverse relationship between ensemble performance, in AUCPR, and average reconstruction error on training samples. Due to space limitations we show these plots only...

Jul 8, 2024 · train the autoencoder, and use the encoder output (as reduced dimension) to train an OC SVM OR use the whole trained autoencoder, calculate the reconstruction error, and use that with a threshold to detect an anomaly.