Discriminative Autoencoders for Small Targets Detection

Abstract

This paper introduces the new concept of discriminative auto encoders. In contrast with the standard auto encoders - which are artificial neural networks used to learn compressed representation for a set of data - discriminative auto encoders aim at learning low-dimensional discriminant encodings using two classes of data (denoted such as the positive and the negative classes). More precisely, the discriminative auto encoders build a latent space (manifold) under the constraint that the positive data should be better reconstructed than the negative data. It can therefore be seen as a generative model of the discriminative data and hence can be used favorably in classification tasks. This new representation is validated on a target detection task, on which the discriminative auto encoders not only give better results than the standard auto encoders but are also competitive when compared to standard classifiers such as the Support Vector Machine.