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相关概念视频

Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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When crossing pea plants, Mendel noticed that one of the parental traits would sometimes disappear in the first generation of offspring, called the F1 generation, and could reappear in the next generation (F2). He concluded that one of the traits must be dominant over the other, thereby causing masking of one trait in the F1 generation. When he crossed the F1 plants, he found that 75% of the offspring in the F2 generation had the dominant phenotype, while 25% had the recessive phenotype.
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The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
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The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
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相关实验视频

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没有保存的重复:为类增量语义分割的原型导出和分布再平衡.

Jinpeng Chen, Runmin Cong, Yuxuan Luo

    IEEE transactions on pattern analysis and machine intelligence
    |March 3, 2025
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    概括

    阶级增量语义细分 (CISS) 方法与阶级失衡作斗争. 我们的STAR方法重复原型并使用新的损失来保持旧知识,同时学习新课程,实现最先进的结果.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 类增量语义细分 (CISS) 允许逐步学习新类,同时保留旧类的知识.
    • 班级失衡是CISS的一个重大挑战,由于训练数据偏差,导致对新学习的班级的偏见.
    • 当前的CISS方法往往无法充分解决失衡问题,导致性能下降.

    研究的目的:

    • 提出一种新的CISS方法,STAR,有效地解决阶级不平衡问题.
    • 开发一个原型重播策略,重新整合过去的类信息,而不需要额外的存储.
    • 引入新的损失函数,保留旧的类特征,并改善类似类之间的歧视.

    主要方法:

    • STAR方法利用原型重复通过重新引入以前类的缺失比例到当前的培训样本.
    • 开发了一种原型偏差技术,以推断过去的类原型,整合分类器和特征提取器模式.
    • 引入了两种新的损失函数,即旧类特征维护 (OCFM) 和相似感知差别 (SAD) 损失,以执行跨任务特征约束.

    主要成果:

    • 在Pascal VOC 2012和ADE20 K数据集上的实验证明了STAR的有效性.
    • 拟议的方法在类增量语义细分方面实现了最先进的性能.

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  • 星号成功地减轻了类不平衡,并保留了以前学习的类的知识.

    • 结论:

    • 在CISS中,STAR提供了一个强大的解决方案来应对阶级不平衡的挑战.
    • 原型重播和新浪损失功能有助于提高性能和知识保留.
    • 这项研究推进了用于语义细分的增量学习领域.