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相关概念视频

Observational Learning01:12

Observational Learning

209
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...
209
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior 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...
441
Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Nonconscious Mimicry01:13

Nonconscious Mimicry

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Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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相关实验视频

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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为持续的无监督域名适应进行生成外观重播.

Boqi Chen1, Kevin Thandiackal2, Pushpak Pati3

  • 1ETH AI Center, Zurich, Switzerland; Department of Computer Science, ETH Zurich, Switzerland.

Medical image analysis
|August 19, 2023
PubMed
概括

这项研究介绍了持续域调整 (GarDA) 的生成外观重复,这是一种用于无监督细分的新方法. GarDA将模型顺序调整到新的数据域,而不需要过去的数据,克服隐私和数据限制.

关键词:
心脏细分是指心脏的细分.持续的学习 持续的学习光学磁盘细分的细分方法前列腺细分是指前列腺的细分.无监督的域名适应

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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:

  • 深度学习在大型标记数据集中表现出色,但现实世界的数据带来了诸如增量可用性,域名转移和缺乏标签等挑战.
  • 医疗应用程序面临严格的隐私法规,禁止保留以前看到的数据.
  • 无监督域调整 (UDA) 对于在不同域中利用无标签数据至关重要.

研究的目的:

  • 在涉及领域转移的持续学习场景中,解决无监督细分的挑战.
  • 开发一种方法,通过使用未标记的数据,将细分模型连续适应到新领域.
  • 为了使适应不需要访问先前看到的数据,增强实际适用性.

主要方法:

  • 介绍了GarDA (Generative Appearance Replay for Continual Domain Adaptation),一种基于生成重复的方法. 这是一个基于生成重复的方法.
  • 对细分模型的顺序适应新领域的未标记数据.
  • 通过不断的调整,利用和整合来自多个领域的信息.

主要成果:

  • 在多个领域的无监督细分方面,GarDA的性能远远超过现有的技术.
  • 该方法在持续学习场景中表现出有效性,具有域名转移.
  • 在涉及不同机关和方式的三个数据集上成功进行了评估.

结论:

  • GarDA为隐私受限,持续学习设置中的无监督细分提供了一个实用的解决方案.
  • 生成式重播方法有效地处理域名转移而无需数据保留.
  • 这项工作通过消除对事先数据访问的需求,推动了增量UDA领域的发展.