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相关概念视频

Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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相关实验视频

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ProtoCLIP:原型的对比语言图像预训练

Delong Chen, Zhao Wu, Fan Liu

    IEEE transactions on neural networks and learning systems
    |December 4, 2023
    PubMed
    概括

    原型对比语言图像预训练 (ProtoCLIP) 增强了表示分组,以提高效率和稳定性. 这种方法可以提高下游任务的性能,并且与CLIP相匹配.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 对比性语言图像预训练 (CLIP) 擅长将学习到的表征转移到下游任务中.
    • CLIP的InfoNCE目标是对准正图像-文本对并分离负图像,间接分组相似的表示.
    • 在CLIP中通过模式内点观察到一个潜在的代表性分组效应.

    研究的目的:

    • 引入原型对比语言图像预训 (ProtoCLIP) 以提高表示分组的效率和稳定性.
    • 改善多式联络模式中的转移学习能力.
    • 为了解决代表性学习中的模式差距.

    主要方法:

    • ProtoCLIP 建立了在图像和文本空间之间进行原型级别的区分,以实现高效的知识传输.
    • 原型反向翻译 (PBT) 将表示分组与对齐脱,使得在很大的模式差距下学习成为可能.
    • 在线插曲式培训策略允许扩展到无限数据,并结合外部知识来源.

    主要成果:

    • 在概念捕捉数据集上,ProtoCLIP实现了 +5.81%的ImageNet线性探测改进和 +2.01%的ImageNet零射击分类改进.
    • 在YFCC-15M数据集中,ProtoCLIP使用只有33%的培训时间,与CLIP的表现相匹配.

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  • 提出的方法表明,尽管存在显著的模式差距,但有效地学习了有意义的表示.

    • 结论:

    • ProtoCLIP提供了一种更有效,更强大的方法来进行对比性语言形象预训练.
    • 通过PBT将分组与对齐脱是处理模式差距和整合外部知识的关键.
    • ProtoCLIP为现有的预训练方法提供了可扩展和有效的替代方案,减少了训练时间和提高了性能.