Improving Classi cation via Reconstruction
Learning a many-parameter model is generally an under-constrained problem that requires additional regularization. We propose to use reconstruction as a regularization constraint for image classiication. We show that fusing the two models together is an eeective regularizer which adds to the improvement achieved by weight decay constraints. This regularization is eeective for single networks and network ensembles. Classiication results are demonstrated on two facial data-sets which are extended to include various image degradations. We show that combining weight decay and reconstruction constraints improves image classiication for a wide range of degradations. In particular, \bagging" ensembles which are composed of regularized networks trained on diierent cross-folds produce best results.