New preprint: latent-entropy anomaly detection

In collaboration with ECE colleagues, we have put out a new variant of our unsupervised anomaly detection pipeline. This one uses a latent entropy loss to scramble the latent space, making anomalies harder to reconstruct (and hence easier to detect).

No supervision (including a normal-only dataset) needed!

Citation:

• Yuang Geng, Junkai Zhou, Kang Yang, Pan He, Zhuoyang Zhou, Jose C. Principe, Joel Harley, Ivan Ruchkin.
  MLE-UVAD: Minimal Latent Entropy Autoencoder for Fully Unsupervised Video Anomaly Detection [Arxiv].
  Preprint, 2026.