Learning with Hierarchical-Deep Models

R. Salakhutdinov, J. B. Tenenbaum, A. Torralba
2013 IEEE Transactions on Pattern Analysis and Machine Intelligence  
We introduce HD (or "Hierarchical-Deep") models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) models. Specifically, we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a deep Boltzmann machine (DBM). This compound HDP-DBM model learns to learn novel concepts from very few training example by learning low-level generic features, high-level features that
more » ... apture correlations among low-level features, and a category hierarchy for sharing priors over the high-level features that are typical of different kinds of concepts. We present efficient learning and inference algorithms for the HDP-DBM model and show that it is able to learn new concepts from very few examples on CIFAR-100 object recognition, handwritten character recognition, and human motion capture datasets.
doi:10.1109/tpami.2012.269 pmid:23787346 fatcat:ecdmqr225nfati75px3fbcocaa