Skip to Content

Feature Learning by Large-Scale Heterogeneous Networks with Application to Face Verification

Thomas S. Huang, University of Illinois at Urbana-Champaign

Usage Details

Thomas S. Huang, Jiangping Wang, Wei Han, Thomas Paine, Zhaowen Wang

Feature learning lies at the core of all visual recognition problems, which are as diverse as object categorization, scene understanding, video event recognition, face verification, and soft- biometrics. To date, one of the most successful approaches for feature leaning is Deep Neural Networks (DNN) (aka deep learning). However existing deep learning methods are limited; they are not able to actively model semantics present at many levels in the image. This project focuses on the Heterogeneous Network (HN) for learning image features. As a natural extension to the recently popular Deep Neural Network, HN perfectly accommodates the structural image semantics via a novel layer-wise training strategy. Existing research indicates that the learning of large-scale, many-layer neural networks is computationally expensive. Here we propose a distributed learning framework for HNs using Asynchronous Stochastic Gradient Descent (ASGD), which scales up to thousands of computers.



http://www.ece.illinois.edu/directory/profile/t-huang1