3 citations · 3 across the 4 of their papers we have counts for
5 papers
A Deep Moving-camera Background Model
Guy Erez, Ron Shapira Weber, Oren Freifeld
In video analysis, background models have many applications such as background/foreground separation, change detection, anomaly detection, tracking, and more. However, while learni…
CPU- and GPU-based Distributed Sampling in Dirichlet Process Mixtures for Large-scale Analysis
Or Dinari, Raz Zamir, John W. Fisher +1
In the realm of unsupervised learning, Bayesian nonparametric mixture models, exemplified by the Dirichlet Process Mixture Model (DPMM), provide a principled approach for adapting…
DeepDPM: Deep Clustering With an Unknown Number of Clusters
Meitar Ronen, Shahaf E. Finder, Oren Freifeld
Deep Learning (DL) has shown great promise in the unsupervised task of clustering. That said, while in classical (i.e., non-deep) clustering the benefits of the nonparametric appro…
Common Failure Modes of Subcluster-based Sampling in Dirichlet Process Gaussian Mixture Models -- and a Deep-learning Solution
Vlad Winter, Or Dinari, Oren Freifeld
The Dirichlet Process Gaussian Mixture Model (DPGMM) is often used to cluster data when the number of clusters is unknown. One main DPGMM inference paradigm relies on sampling. Her…
Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data
Or Dinari, Oren Freifeld
Practical tools for clustering streaming data must be fast enough to handle the arrival rate of the observations. Typically, they also must adapt on the fly to possible lack of sta…