β-VAE models for unsupervised anomaly detection show a reconstruction-detection trade-off mitigated by beta-scheduling and Sparse VAE.
Second, we analyze the performance vari- ability across different training seeds and find that it is linked with the distance between healthy and abnormal latent dis- tributions
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Mitigating the reconstruction-detection trade-off in VAE-based unsupervised anomaly detection
β-VAE models for unsupervised anomaly detection show a reconstruction-detection trade-off mitigated by beta-scheduling and Sparse VAE.