Fast DPCNs for Feature Extraction without Labels

Deep predictive coding networks (DPCNs) effectively model and capture video features through a bi-directional inference without labels. They are based on an overcomplete description of video scenes, and one of the bottlenecks has been the lack of effective sparsification techniques to find discriminative and robust dictionaries. This paper proposes a DPCN with a fast inference of internal dictionaries and variables that achieve high sparsity and feature clustering accuracy. The proposed unsupervised learning procedure uses majorization-minimization (MM) to smooth sparsity constraints in optimization and admits explainability and convergence. Experiments in the image and video data sets CIFAR-10, Super Mario Bros, and Coil-100 validate that the approach outperforms previous versions of DPCNs on learning rate, sparsity ratio, and feature clustering accuracy. This advance opens the door for general applications in object recognition in video without labels.