mlsanityData to 7 Oct 2026

Negative Log Likelihood Ratio Loss for Deep Neural Network Classification

arXiv 1804.10690 ↗First posted 27 Apr 2018cs.LG stat.MLcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Apr 2018.

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Citing papers per year: 2021: 1; 2022: 2; 2023: 5; 2024: 3; 2025: 5; 2026: 1.

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