A 3D convolutional variational autoencoder trained on bulk spectra detects synthetic Fe L-edge peak-shift anomalies in EELS spectrum images with higher F1 scores than PCA.
Through hy- perparameter tuning, we determined β = 1.2 to provide the optimal balance between latent space organization and reconstruction quality for this specific dataset
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Robust Spectral Anomaly Detection in EELS Spectral Images via Three Dimensional Convolutional Variational Autoencoders
A 3D convolutional variational autoencoder trained on bulk spectra detects synthetic Fe L-edge peak-shift anomalies in EELS spectrum images with higher F1 scores than PCA.