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Dimensionality Reduction for Flow-Based Face Embeddings

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Advances in Distributed Computing and Machine Learning

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 127))

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Abstract

Flow-based neural networks are promising generative image models. One of their main drawbacks at the moment is the large number of parameters and the large size of the hidden representation of modeled data, which complicates their use on an industrial scale. This article proposes a method for isolating redundant components of vector representations generated by flow-based networks using the Glow neural network as an example to the face generation problem, which has shown effective ten times compression. The prospects of using such compression for more efficient parallelization of training and inference using model parallelism are considered.

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Acknowledgements

This research work was supported by the Academic Excellence Project 5-100 proposed by Peter the Great St. Petersburg Polytechnic University.

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Authors and Affiliations

  1. Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation

    S. Poliakov & I. Belykh

Authors
  1. S. Poliakov
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  2. I. Belykh
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Corresponding author

Correspondence to I. Belykh .

Editor information

Editors and Affiliations

  1. Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Asis Kumar Tripathy

  2. San Diego State University, San Diego, CA, USA

    Mahasweta Sarkar

  3. Institute of Technical Education and Research (ITER), Siksha ‘O’ Anusandhan (SOA) Deemed to be University, Bhubaneswar, Odisha, India

    Jyoti Prakash Sahoo

  4. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. National Institute of Technology, Rourkela, Rourkela, Odisha, India

    Suchismita Chinara

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Poliakov, S., Belykh, I. (2021). Dimensionality Reduction for Flow-Based Face Embeddings. In: Tripathy, A., Sarkar, M., Sahoo, J., Li, KC., Chinara, S. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 127. Springer, Singapore. https://doi.org/10.1007/978-981-15-4218-3_36

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