  Conventionally, autoencoders are unsupervised representation learning tools.
In this work, we propose a novel discriminative autoencoder. Use of supervised
discriminative learning ensures that the learned representation is robust to
variations commonly encountered in image datasets. Using the basic
discriminating autoencoder as a unit, we build a stacked architecture aimed at
extracting relevant representation from the training data. The efficiency of
our feature extraction algorithm ensures a high classification accuracy with
even simple classification schemes like KNN (K-nearest neighbor). We
demonstrate the superiority of our model for representation learning by
conducting experiments on standard datasets for character/image recognition and
subsequent comparison with existing supervised deep architectures like class
sparse stacked autoencoder and discriminative deep belief network.
