Reviews: Deep Structured Prediction with Nonlinear Output Transformations

This paper studies the problem of training deep structured models (models where the dependencies between the output variables are explicitly modelled and some components are modelled via neural networks). The key idea of this paper is to give up the standard modelling assumption of structured prediction: the score (or the energy) function is the sum of summands (potentials). Instead of using the sum the paper puts an arbitrary non-linear (a neural network) transformation on top of the potentials. The paper develops an inference (MAP prediction) technique for such models which is based on Lagrangian decomposition (often referred to as dual decomposition, see details below). The training of the model is done by combining this inference technique with the standard Structure SVM (SSVM) objective.