Improving machine vision via incorporating expectation-maximization into Deep Spatio-Temporal learning
Min Jiang, Yulong Ding, Ben Goertzel, Zhongqiang Huang, Changle Zhou, Fei Chao · 2014
The Deep Spatio-Temporal Inference Network (DeSTIN) is a deep learning architecture which combines un-supervised learning and Bayesian inference. The original version of DeSTIN incorporates k-means clustering inside each processing node. Here we propose to replace k-means with a more sophisticated algorithm, online EM (Expectation Maximization), and show that this improves DeSTIN's performance on image classification and restoration tasks.