Issue No. 08 - Aug. (2013 vol. 35)
R. Salakhutdinov , Dept. of Stat. & Comput. Sci., Univ. of Toronto, Toronto, ON, Canada
J. B. Tenenbaum , Dept. of Brain & Cognitive Sci., Massachusetts Inst. of Technol., Cambridge, MA, USA
A. Torralba , Comput. Sci. & Artificial Intell. Lab., Massachusetts Inst. of Technol., Cambridge, MA, USA
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 capture 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.
Approximation methods, Machine learning, Stochastic processes, Computational modeling, Vectors, Bayesian methods, Training
R. Salakhutdinov, J. B. Tenenbaum and A. Torralba, "Learning with Hierarchical-Deep Models," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. 8, pp. 1958-1971, 2013.