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Updated: Jul 13, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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无监督域名适应与不对称的边际差异损失和异常值样本提取

Chunmei He1, Xianjun Fan1, Kang Zhou1

  • 1School of Computer Science, School of Cyberspace Science, Xiangtan University, Xiangtan, Hunan 411105, China.

Neural networks : the official journal of the International Neural Network Society
|October 15, 2023
PubMed
概括

本研究介绍了AMD-Net与OSE,这是一种新的无监督域调整方法. 它通过解决令人困惑的目标样本和源域异常值来改善特征提取,实现最先进的性能.

关键词:
无监督的域名适应具有对抗性的学习.深度神经网络是一个神经网络.图像的分类图像的分类.转移学习转移学习

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