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. 2022 Apr 19;13:2032. doi: 10.1038/s41467-022-29763-x

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — The mean values of AUC scores on MIND and F1 scores on ADR as well as their 95% confidence intervals are illustrated (n = 5 independent experiments). We compare the performance of mentors and the four-layer or two-layer mentee models when mutual distillation (MD) is used or not. The results show that mutual distillation can improve the performance of both mentee and mentor models, which is because useful knowledge can be reciprocally transferred between the mentee and mentor.