Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

445
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...
445
Aggregates Classification01:29

Aggregates Classification

305
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
305
Classification of Systems-I01:26

Classification of Systems-I

169
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
169
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

31.7K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
31.7K
Classification of Systems-II01:31

Classification of Systems-II

134
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
134
Classification of Signals01:30

Classification of Signals

403
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
403

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Hierarchical feature based dual contrastive multiview clustering.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Unsupervised feature selection via row-sparse local preserving projection.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

A Unified Framework for Pseudo-Supervised Clustering via Weighted Sample Aggregation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Projection with mixed-size anchor graphs.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

SimMTC: Simple Multi-View Tensor Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Unsupervised fine-tuning of vision-language models by fusing classifier tuning and visual prompt tuning.

Neural networks : the official journal of the International Neural Network Society·2026

相关实验视频

Updated: Jun 7, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K

具有分布一致性规范化的原型分类器用于一般化类别发现:强大的基线.

Zhanxuan Hu1, Yu Duan2, Yaming Zhang1

  • 1School of Information Science and Technology, Yunnan Normal University, Chenggong, Kunming, 650500, Yunnan, China.

Neural networks : the official journal of the International Neural Network Society
|November 21, 2024
PubMed
概括

本研究引入了分布一致性规范化 (DCR),以改进通用类别发现 (GCD) 的原型分类器. DCR通过确保一致的类分布来增强语义理解,从而提高视觉识别任务的性能.

关键词:
集群集成是指集群集成.一般化的类别发现发现.半监督学习 半监督学习没有监督的学习学习.

更多相关视频

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.1K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

相关实验视频

Last Updated: Jun 7, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K
Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.1K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

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:

  • 一般化类别发现 (GCD) 是一个具有挑战性的半监督的视觉识别任务.
  • 原型分类器对GCD是有效的,但通常使用基于Softmax的交叉损失 (SCE),这与类内关系斗争并导致语义模两可.

研究的目的:

  • 提出分布一致性规范化 (DCR) 方案,以增强GCD的原型分类器.
  • 提高分类器捕捉局部结构的能力,减少未标记数据中的语义模两可.

主要方法:

  • 引入分布一致性规范化 (DCR) 作为原型分类器的类内一致性损失.
  • 采用部分标签,而不是硬伪标签,以减轻来自未标签数据的杂监督信号.
  • 开发了一种简洁高效的方法,不需要外部复杂的模块.

主要成果:

  • 在6个基准指标中,DCR持续提高了性能.
  • 与现有方法相比,提出的方法取得了竞争力或优异的结果.
  • 改进后的模型更好地捕捉了局部结构,并减轻了语义模两可.

结论:

  • 分布一致性规范化 (DCR) 是在通用类别发现 (GCD) 中改进原型分类器的有效方法.
  • 该方法为GCD任务提供了一个强大,高效和简洁的基准.
  • 使用部分标签进一步提高了对噪音数据的稳定性.