Deep hybrid networks with good out-of-sample object recognition

Abstract

We introduce Deep Hybrid Networks that are robust to the recognition of out-of-sample objects, i.e., ones that are drawn from a different probability distribution from the training data distribution. The networks are based on a particular combination of an auto-encoder and stacked Restricted Boltzmann Machines (RBMs). The autoencoder is used to extract sparse features, which are expected to be noise invariant in the observations. The stacked RBMs then observe the sparse features as inputs to learn the top hierarchical features. The use of RBMs is motivated by the fact that the stacked RBMs typically provide good performance when dealing with in-sample observations, as proven in the previous works. To improve the robustness against local noise, we propose a variant of our hybrid network by the usage of a mixture of sparse features and sparse connections in the auto-encoder layer. The experiments show that our proposed deep networks provide good performance in both the in-sample and out-of-sample situations, particularly when the number of training examples is small.