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相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
452
Observational Learning01:12

Observational Learning

213
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
213

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相关实验视频

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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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高效的主动学习与Tracklet-Aware协同合作注释器用于人重新识别.

Xiao Teng, Long Lan, Jing Zhao

    IEEE transactions on neural networks and learning systems
    |July 6, 2023
    PubMed
    概括

    这项研究引入了一种可追踪的框架,以减少人重新识别 (ReID) 的人类注释. 该方法使用合作注释器和积极学习来实现更低成本的竞争性表现.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习

    背景情况:

    • 监督人员重新识别 (ReID) 显示出巨大的潜力,但受到高的人类注释成本的限制.
    • 在不同的摄像头上对相同的行人进行注释是昂贵且耗时的.

    研究的目的:

    • 提出一个新的框架,大大减少了对个人ReID的注释需求.
    • 为了保持高性能的人 ReID,同时最大限度地减少对人类投入的需求.

    主要方法:

    • 开发了一个Tracklet意识的合作注释者框架.
    • 训练样本是集群的,相邻的图像形成了强大的轨道,以减少注释需求.
    • 使用教师模型的积极学习策略选择了人类注释的信息轨迹,同时教师也标记了某些轨迹.

    主要成果:

    • 拟议的框架在受欢迎人ReID数据集上实现了竞争性表现.
    • 该方法在积极学习和无监督学习 (USL) 环境中都表现出有效性.
    • 该方法成功地使用伪标签和人类注释训练模型.

    结论:

    • 轨迹感知协作注释者框架有效地降低了ReID.
    • 这种方法为具有有限注释预算的大规模个人ReID应用提供了可行的解决方案.

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  • 积极学习和伪标签的整合提高了模型培训的效率和性能.