Figure 1

Deep learning model overview. The neural network is responsible for learning the feature representation \(\Phi _{\theta }({\mathbf {X}})\), given the input \({\mathbf {X}}\). The parameters of all k distributions, \(\beta _k\), and their mixing weights, \(w_k\), are learned jointly during the training.