  Autoencoders are unsupervised deep learning models used for learning
representations. In literature, autoencoders have shown to perform well on a
variety of tasks spread across multiple domains, thereby establishing
widespread applicability. Typically, an autoencoder is trained to generate a
model that minimizes the reconstruction error between the input and the
reconstructed output, computed in terms of the Euclidean distance. While this
can be useful for applications related to unsupervised reconstruction, it may
not be optimal for classification. In this paper, we propose a novel Supervised
COSMOS Autoencoder which utilizes a multi-objective loss function to learn
representations that simultaneously encode the (i) "similarity" between the
input and reconstructed vectors in terms of their direction, (ii)
"distribution" of pixel values of the reconstruction with respect to the input
sample, while also incorporating (iii) "discriminability" in the feature
learning pipeline. The proposed autoencoder model incorporates a Cosine
similarity and Mahalanobis distance based loss function, along with supervision
via Mutual Information based loss. Detailed analysis of each component of the
proposed model motivates its applicability for feature learning in different
classification tasks. The efficacy of Supervised COSMOS autoencoder is
demonstrated via extensive experimental evaluations on different image
datasets. The proposed model outperforms existing algorithms on MNIST,
CIFAR-10, and SVHN databases. It also yields state-of-the-art results on
CelebA, LFWA, Adience, and IJB-A databases for attribute prediction and face
recognition, respectively.
