4 papers
MLE-UVAD: Minimal Latent Entropy Autoencoder for Fully Unsupervised Video Anomaly Detection
Yuang Geng, Junkai Zhou, Kang Yang +5
In this paper, we address the challenging problem of single-scene, fully unsupervised video anomaly detection (VAD), where raw videos containing both normal and abnormal events are…
Time-Series Classification with Multivariate Statistical Dependence Features
Yao Sun, Bo Hu, Jose Principe
In this paper, we propose a novel framework for non-stationary time-series analysis that replaces conventional correlation-based statistics with direct estimation of statistical de…
A Stable Neural Statistical Dependence Estimator for Autoencoder Feature Analysis
Bo Hu, Jose C Principe
Statistical dependence measures like mutual information is ideal for analyzing autoencoders, but it can be ill-posed for deterministic, static, noise-free networks. We adopt the va…
Fast Deep Predictive Coding Networks for Videos Feature Extraction without Labels
Wenqian Xue, Chi Ding, Jose Principe
Brain-inspired deep predictive coding networks (DPCNs) effectively model and capture video features through a bi-directional information flow, even without labels. They are based o…