Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks

Understand

Ensemble methods, traditionally built with independently trained de-correlated models, have proven to be efficient methods for reducing the remaining residual generalization error, which results in robust and accurate methods for real-world applications.

• In the context of deep learning, however, training an ensemble of deep networks is costly and generates high redundancy which is inefficient.
• In this paper, we present experiments on Ensembles with Shared Representations (ESRs) based on convolutional networks to demonstrate, quantitatively and qualitatively, their data processing efficiency and scalability to large-scale datasets of facial expressions.
• We show that redundancy and computational load can be dramatically reduced by varying the branching level of the ESR without loss of diversity and generalization power, which are both important for ensemble performance.