An autoencoder with minimal latent entropy loss enables fully unsupervised video anomaly detection by concentrating normal latent embeddings and producing poor reconstructions for anomalies.
In: 2022 IEEE International Conference on Multimedia and Expo (ICME)
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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.