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Here we plotted the distribution/PDF plot of the reconstruction error to show the novelty detection performance. As you can see we obtained a clear separation between the reconstruction error of known and novel signals. We used an n-sigma thresholding approach. We determine the threshold from the mean and standard deviation of the reconstruction error of the known signals. This simple approach worked quite well for novelty detection in this case.
We also compared the novelty detection performance of our framework against the SoA algorithms such as Saife, GAN (generative adversarial network), OSVM (one class SVM), IFO (isolation forest), and LODA (Lightweight on-line detector of anomalies). Saife provided a close novelty detection performance compared to our AE-based model. Saife is also a reconstruction-based method. With our proposed framework, we obtained 100% novelty detection accuracy at 1.04% FAR on the known signals whereas Saife provided 3.81% FAR. The IFO and OSVM provided similar performances. The GAN and Loda provided worse performance compared to other frameworks.
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IEEE/ICACT20230149 Slide.14
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