  The comparison of heterogeneous samples extensively exists in many
applications, especially in the task of image classification. In this paper, we
propose a simple but effective coupled neural network, called Deeply Coupled
Autoencoder Networks (DCAN), which seeks to build two deep neural networks,
coupled with each other in every corresponding layers. In DCAN, each deep
structure is developed via stacking multiple discriminative coupled
auto-encoders, a denoising auto-encoder trained with maximum margin criterion
consisting of intra-class compactness and inter-class penalty. This single
layer component makes our model simultaneously preserve the local consistency
and enhance its discriminative capability. With increasing number of layers,
the coupled networks can gradually narrow the gap between the two views.
Extensive experiments on cross-view image classification tasks demonstrate the
superiority of our method over state-of-the-art methods.
