An autoencoder with minimal latent entropy loss enables fully unsupervised video anomaly detection by concentrating normal latent embeddings and producing poor reconstructions for anomalies.
IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(9), 5819–5829 (2019)
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MLE-UVAD: Minimal Latent Entropy Autoencoder for Fully Unsupervised Video Anomaly Detection
An autoencoder with minimal latent entropy loss enables fully unsupervised video anomaly detection by concentrating normal latent embeddings and producing poor reconstructions for anomalies.