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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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相关实验视频

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RepCo: 补充样本视图以更好的一致性,用于对比学习.

Xinyu Lei1, Longjun Liu1, Yi Zhang1

  • 1National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center of Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.

Neural networks : the official journal of the International Neural Network Society
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概括

RepCo通过从同一图像中创建更好的样本对来增强自主监督学习 (SSL),提高对象检测和语义细分等下游任务的表示质量.

关键词:
相反的学习学习.采样策略 采样策略自主监督的预训.

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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:

  • 对比式学习方法通过比较样本对来学习表示.
  • 当前的方法经常使用随机的图像裁剪,导致不一致的语义信息和低于最佳的性能.
  • 设计有效的样本对对于改善对比学习至关重要.

研究的目的:

  • 提出一个新的自我监督学习 (SSL) 框架,RepCo,以解决对比学习的样本对生成的局限性.
  • 通过探索单一图像中的语义一致性来提高对比视图的质量.
  • 为下游任务生成更具信息性的表示.

主要方法:

  • RepCo从同一图像中选择补丁,以形成正负对,与以前的方法不同,依赖于不同视图的随机裁剪.
  • 它鼓励来自不同位置的相似贴片成为正对.
  • 它强制从相邻位置的不相似补丁成为负对,丰富学习的表示.

主要成果:

  • 通过利用图像内部的语义一致性,RepCo有效地产生了高质量的对比视图.
  • 该框架提供了更多信息的表示,有利于各种下游任务.
  • 在对象检测 (例如,Pascal VOC上的+2.1 AP50) 和语义细分 (例如,Cityscapes上的+2.3 mIoU) 中观察到显著的性能增长.

结论:

  • RepCo提供了一种新的自我监督表示学习方法,通过智能地从图像中构建样本对.
  • 该方法克服了与传统对比学习中的随机裁剪相关的语义不一致问题.
  • 在关键计算机视觉任务上,RepCo表现出强的性能改进,突出显示了它的有效性.