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Computer Science > Computer Vision and Pattern Recognition

arXiv:2109.02345 (cs)
[Submitted on 6 Sep 2021]

Title:Tensor Normalization and Full Distribution Training

Authors:Wolfgang Fuhl
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Abstract:In this work, we introduce pixel wise tensor normalization, which is inserted after rectifier linear units and, together with batch normalization, provides a significant improvement in the accuracy of modern deep neural networks. In addition, this work deals with the robustness of networks. We show that the factorized superposition of images from the training set and the reformulation of the multi class problem into a multi-label problem yields significantly more robust networks. The reformulation and the adjustment of the multi class log loss also improves the results compared to the overlay with only one class as label. this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2109.02345 [cs.CV]
  (or arXiv:2109.02345v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2109.02345

Submission history

From: Wolfgang Fuhl [view email]
[v1] Mon, 6 Sep 2021 10:33:17 UTC (59 KB)
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