  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. Experiments on large-scale
datasets suggest that ESRs reduce the remaining residual generalization error
on the AffectNet and FER+ datasets, reach human-level performance, and
outperform state-of-the-art methods on facial expression recognition in the
wild using emotion and affect concepts.
