Encoding and decoding representations with sum- And max-product networks

Antonio Vergari, Robert Peharz, Nicola Di Mauro, Floriana Esposito

Research output: Contribution to conferencePaper

Abstract

Sum-Product Networks (SPNs) are deep density estimators allowing exact and tractable inference. While up to now SPNs have been employed as black-box inference machines, we exploit them as feature extractors for unsupervised Representation Learning. Representations learned by SPNs are rich probabilistic and hierarchical part-based features. SPNs converted into Max-Product Networks (MPNs) provide a way to decode these representations back to the original input space. In extensive experiments, SPN and MPN encoding and decoding schemes prove highly competitive for Multi-Label Classification tasks.

Original languageEnglish
Publication statusPublished - 1 Jan 2019

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