On Feature Diversity in Energy-based Models

Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj

Keywords: energy-based models, redundancy reduction, feature diversity

TL;DR: We derive generalization bounds for energy-based models showing that reducing the redundancy of the feature can lead to better generalization

Abstract: Energy-based learning is a powerful learning paradigm that encapsulates various discriminative and generative approaches. An energy-based model (EBM) is typically formed of inner-model(s) that learn a combination of the different features to generate an energy mapping for each input configuration. In this paper, we focus on the diversity of the produced feature set. We extend the probably approximately correct (PAC) theory of EBMs and analyze the effect of redundancy reduction on the performance of EBMs. We derive generalization bounds for various learning contexts, i.e., regression, classification, and implicit regression, with different energy functions and we show that indeed reducing redundancy of the feature set can consistently decrease the gap between the true and empirical expectation of the energy and boosts the performance of the model.

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