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Conference material: "Proceedings of the International Conference on Computer Graphics and Vision Graphicon"
Authors: Penkin M.A., Khvostikov A.V., Krylov A.S.
Optimal Input Scale Transformation Search for Deep Classification Neural Networks
Abstract:
The paper deals with problem of optimal input scale search for deep classification neural networks. It is shown that state-of-the-art deep neural networks are not stable to input image scale, leading to quality degradation. The paper demonstrates relevance of the topic on classical image classification DL-pipeline. Unlike previous researchers, who aim to build entire complex invariant neural nets, we claim that computing optimal input transformations (e.g. scale) is a more perspective way for successful neural networks real-life applications. Thus, a new scale search algorithm for DL image classification is proposed in the paper, based on empirical hierarchical analysis of activation values.
Keywords:
Image scale estimation, Deep learning, Image classification, Medical imaging
Publication language: english,  pages: 10 (p. 668-677)
English source text:
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About authors:
  • Penkin Maksim Alexandrovich,  orcid.org/0000-0002-8027-9333,  Lomonosov Moscow State University
  • Khvostikov Alexander Vladimirovich,  orcid.org/0000-0002-4217-7141,  Lomonosov Moscow State University
  • Krylov Andrey Serdjevich,  orcid.org/0000-0001-9910-4501,  Lomonosov Moscow State University