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A Survey of Loss Functions in Deep Learning

Author

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  • Caiyi Li

    (School of Educational Science, Hunan Normal University, Changsha 410081, China
    Institute of Interdisciplinary Studies, Hunan Normal University, Changsha 410081, China)

  • Kaishuai Liu

    (School of Educational Science, Hunan Normal University, Changsha 410081, China
    Institute of Interdisciplinary Studies, Hunan Normal University, Changsha 410081, China)

  • Shuai Liu

    (School of Educational Science, Hunan Normal University, Changsha 410081, China
    Institute of Interdisciplinary Studies, Hunan Normal University, Changsha 410081, China)

Abstract

Deep learning (DL), as a cutting-edge technology in artificial intelligence, has significantly impacted fields such as computer vision and natural language processing. Loss function determines the convergence speed and accuracy of the DL model and has a crucial impact on algorithm quality and model performance. However, most of the existing studies focus on the improvement of specific problems of loss function, which lack a systematic summary and comparison, especially in computer vision and natural language processing tasks. Therefore, this paper reclassifies and summarizes the loss functions in DL and proposes a new category of metric loss. Furthermore, this paper conducts a fine-grained division of regression loss, classification loss, and metric loss, elaborating on the existing problems and improvements. Finally, the new trend of compound loss and generative loss is anticipated. The proposed paper provides a new perspective for loss function division and a systematic reference for researchers in the DL field.

Suggested Citation

  • Caiyi Li & Kaishuai Liu & Shuai Liu, 2025. "A Survey of Loss Functions in Deep Learning," Mathematics, MDPI, vol. 13(15), pages 1-50, July.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:15:p:2417-:d:1711153
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