Hierarchical compositional feature learning

By Miguel L'azaro-Gredilla, Yi Liu, D. Scott Phoenix, Dileep George. 2016

13 citations recorded by the source corpus.

We introduce the hierarchical compositional network (HCN), a directed generative model able to discover and disentangle, without supervision, the building blocks of a set of binary images. The building blocks are binary features defined hierarchically as a composition of some of the features in the layer immediately below, arranged in a particular manner. At a high level, HCN is similar to a sigmoid belief network with pooling. Inference and learning in HCN are very challenging and existing variational approximations do not work satisfactorily. A main contribution of this work is to show that both can be addressed using max-product message passing (MPMP) with a particular schedule (no EM required). Also, using MPMP as an inference engine for HCN makes new tasks simple: adding supervision information, classifying images, or performing inpainting all correspond to clamping some variables of the model to their known values and running MPMP on the rest. When used for classification, fast inference with HCN has exactly the same functional form as a convolutional neural network (CNN) with linear activations and binary weights. However, HCN’s features are qualitatively very different.

Subfields: Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

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Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, background): “Despite lacking theoretical guarantees, MP has been known to work well in many practical cases, and recently we showed that it can be used for learning of compositional features [6].”