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Negative Log Likelihood Ratio Loss for Deep Neural Network Classification

  • Hengshuai YaoEmail author
  • Dong-lai Zhu
  • Bei Jiang
  • Peng Yu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1069)

Abstract

In deep neural network, the cross-entropy loss function is commonly used for classification. Minimizing cross-entropy is equivalent to maximizing likelihood under assumptions of uniform feature and class distributions. It belongs to generative training criteria which does not directly discriminate correct class from competing classes. We propose a discriminative loss function with negative log likelihood ratio between correct and competing classes. It significantly outperforms the cross-entropy loss on the CIFAR-10 image classification task.

Keywords

Loss function Cross entropy Likelihood ratio Deep neural network 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hengshuai Yao
    • 1
    Email author
  • Dong-lai Zhu
    • 2
  • Bei Jiang
    • 3
  • Peng Yu
    • 3
  1. 1.Huawei Hi-SiliconEdmontonCanada
  2. 2.Huawei Noah’s Ark LabEdmontonCanada
  3. 3.Department of Mathematical and Statistical SciencesUniversity of AlbertaEdmontonCanada

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