AutoMarkov DNNs for object classification
Recent advances in the area of Deep Convolutional Neural Networks have led to steady progress, mainly observed in the field of object classification and localization. Extensive testing helped generate frameworks guaranteeing the initiation of successful network architectures. For this reason, the authors focus on bringing added value on specific nodes of a generic network configuration. We propose a novel type of convolutional layer based on Autobinomial Markov-Gibbs Random Fields (AutoMarkov Layer). Our choice is motivated by the fact that each neuron in a layer is only connected to a local region in the following layer. This property allows us to integrate Markov Random Fields into the structure of a neuron, to account for the probability of each particular pathway. Functional testing is performed on the MNIST, CIFAR-10 and CIFAR-100 datasets, showing clear improvements for correct classification scores on all the datasets mentioned regardless of the network architecture.