Search Results for author:
Found 4 papers, 1 papers with code
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models
Abnormal event detection is one of the important objectives in research and practical applications of video surveillance.
Dual Discriminator Generative Adversarial Nets
We develop theoretical analysis to show that, given the maximal discriminators, optimizing the generator of D2GAN reduces to minimizing both KL and reverse KL divergences between data distribution and the distribution induced from the data generated by the generator, hence effectively avoiding the mode collapsing problem.
Ranked #18 on
Image Generation
on STL-10
(Inception score metric)
Energy-based Models for Video Anomaly Detection
Automated detection of abnormalities in data has been studied in research area in recent years because of its diverse applications in practice including video surveillance, industrial damage detection and network intrusion detection.
Geometric Enclosing Networks
Training model to generate data has increasingly attracted research attention and become important in modern world applications.
