Improving Classification 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 classification. We show that fusing the two models together is an effective regularizer which adds to the improvement achieved by weight decay constraints. This regularization is effective for single networks and network ensembles. Classification 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 classification for a wide range of degradations. In particular, “bagging” ensembles which are composed of regularized networks trained on different cross-folds produce best results.