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Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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通过异质的自我监督学习来增强表现.

Zhong-Yu Li, Bo-Wen Yin, Yongxiang Liu

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    此摘要是机器生成的。

    异质自主监督学习 (HSSL) 通过使用不同的架构来增强视觉模型. 增加模型之间的架构差异提高了表示质量,以更好地执行下游任务.

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    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:

    • 结合变压器和卷积的混合网络在视觉任务中很常见.
    • 不同质架构的互补性在自主监督学习中未得到充分探索.

    研究的目的:

    • 引入异质自主监督学习 (HSSL) 以利用架构多样性.
    • 在没有结构修改的情况下,改进基准模型中的表示学习.

    主要方法:

    • 强制执行一个基本模型,从一个具有异质架构的辅助头学习.
    • 用各种异质模型对进行实验,以分析表示质量.
    • 制定最佳辅助头选择和增加模型差异的方法的搜索策略.

    主要成果:

    • 基本模型的表示质量随着建筑差异的增加而提高.
    • HSSL与各种自主监督学习方法兼容.
    • 在图像分类,语义细分,实例细分和对象检测任务中实现了卓越的性能.

    结论:

    • HSSL有效地利用异构架构来增强自我监督的表示学习.
    • 架构上的差异是改善模型性能的一个关键因素.
    • 提出的方法为计算机视觉自主监督学习提供了灵活和有效的方法.