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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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全球交叉损失对深层面部识别的损失

Weisong Zhao, Xiangyu Zhu, Haichao Shi

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |March 5, 2025
    PubMed
    概括

    本研究介绍了全球交叉损失 (GCE),通过增强相似性区别来改善深度面部识别. 拟议的GFace模型在多个基准测试中显示出卓越的性能.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 生物识别信息 生物识别信息

    背景情况:

    • 深度面部识别通常使用Softmax损失,该损失专注于样本类原型相似性.
    • 软max损失忽视了样本内和样本外相似性之间的相互作用,可能限制了模型歧视.
    • 现有的方法主要提高了在样本中的目标相似性,而不是在样本中的非目标相似性.

    研究的目的:

    • 提出一种新的损失函数,全球交叉 (GCE),它解决了深度面部识别中的Softmax损失的局限性.
    • 提高深度面部识别模型的歧视和概括能力.
    • 弥合面部识别系统培训和测试阶段之间的差距.

    主要方法:

    • 引入全球交叉 (GCE) 损失,促进更大的样本目标相似性超过所有非目标相似性和更小的样本非目标相似性到所有目标相似性.
    • 对样本中的目标和非目标相似性实施了双边边际处罚,以促进模型歧视和泛化.
    • 通过将类原型与样本特征替换以调整培训和测试,将GCE损失调整为双向框架.
    • 开发了GFace模型,利用拟议的GCE损失.

    主要成果:

    • 与现有的方法相比,GFace在多个公共面部识别基准 (LFW,CALFW,CPLFW,CFP-FP,AgeDB,IJB-C,IJB-B,MFR-Ongoing,MegaFace) 上取得了优异的性能.

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  • 拟议的GCE损失有效地增强了深层面模型中的歧视和概括.
  • 在面部识别之外的一般视觉识别任务中,GFace表现出强大的性能.

    • 结论:

    • 全球交叉损失函数为深度面部识别提供了比传统Softmax损失显著的进步.
    • 由GCE授权的GFace模型在人脸识别准确性和稳定性方面建立了新的最先进的状态.
    • 拟议的方法显示了在视觉识别任务中更广泛的应用的前景.