Mitigating the reconstruction-detection trade-off in VAE-based unsupervised anomaly detection

ArXi:2605.02918v1 Announce Type: new Variational autoencoders are widely used for unsupervised anomaly detection. Model selection however remains an open-question: to remain fully unsupervised, hyperparameters are often chosen to minimize the reconstruction error on normal samples. In this paper, we reveal a trade-off between reconstruction quality and anomaly detection among $\beta$-VAE models. Models with constrained latent space reach higher detection metrics but lower reconstruction quality.