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AbstractNet: A Generative Model for High Density Inputs

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Machine Learning, Optimization, and Big Data (MOD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10710))

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Abstract

This paper introduces AbstractNet, a generative model for high density inputs. The model suggests a method that uses unsupervised learning to generate feature maps. The model drastically improves the performances of raw audio generation by reducing the required amount of input data and computing power necessary to achieve a similar result when compared to the state of the art.

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Acknowledgements

I want to thank Alain Lioret from Université Paris 8, Aurélien Schlossman from Ariane Group, Nicolas Vidal, Martin Tricaud and everyone at Ecole Superieure De Génie Informatique (ESGI). I would also like to thank all the people who believed in this project.

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Authors and Affiliations

  1. ESGI, 242 Rue du Faubourg Saint-Antoine, 75012, Paris, France

    Boris Musarais

Authors
  1. Boris Musarais
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Correspondence to Boris Musarais .

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Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Musarais, B. (2018). AbstractNet: A Generative Model for High Density Inputs. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science(), vol 10710. Springer, Cham. https://doi.org/10.1007/978-3-319-72926-8_38

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  • DOI: https://doi.org/10.1007/978-3-319-72926-8_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72925-1

  • Online ISBN: 978-3-319-72926-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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