Learning Local Receptive Fields in Deep Belief Networks for Visual Feature Detection

  • Diana Turcsany
  • Andrzej Bargiela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8834)


Through the introduction of local receptive fields, we improve the fidelity of restricted Boltzmann machine (RBM) based representations to encodings extracted by visual processing neurons. Our biologically inspired Gaussian receptive field constraints encourage learning of localized features and can seamlessly integrate into RBMs. Moreover, we propose a method for concurrently finding advantageous receptive field centers, while training the RBM. The strength of our method to reconstruct characteristic details of facial features is demonstrated on a challenging face dataset.


Visual information processing neural encoding deep belief network receptive fields unsupervised learning facial feature detection 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Diana Turcsany
    • 1
  • Andrzej Bargiela
    • 1
  1. 1.School of Computer ScienceThe University of NottinghamNottinghamUnited Kingdom

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